Metastasis of colorectal cancer to the liver is the most common indication for hepatic resection in a western population. Incomplete excision of malignancy due to residual microscopic disease normally results in worse patient outcome. Therefore, a method aiding in the real time discrimination of normal and malignant tissue on a microscopic level would be of benefit.The ability of fluorescent probe‐based confocal laser endomicroscopy (pCLE) to identify normal and malignant liver tissue was evaluated in an orthotopic murine model of colorectal cancer liver metastasis (CRLM). To maximise information yield, two clinical fluorophores, fluorescein and indocyanine green (ICG) were injected and imaged in a dual wavelength approach . Visual tissue characteristics on pCLE examination were compared with histological features. Fluorescence intensity in both tissues was statistically analysed to elucidate if this can be used to differentiate between normal and malignant tissue.Fluorescein (488 nm) enabled good visualisation of normal and CRLM tissue, whereas ICG (660 nm) visualisation was limited to normal liver tissue only. Fluorescence intensity in areas of CRLM was typically 53–100% lower than normal hepatic parenchyma. Using general linear mixed modelling and receiver operating characteristic analysis, high fluorescence intensity was found to be statistically more likely in normal hepatic tissue.Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.Real time discrimination between normal liver parenchyma and metastatic tissue with pCLE examination of fluorescein and ICG is feasible. Employing two (rather than a single) fluorophores allows a combination of qualitative and quantitative characteristics to be used to distinguish between hepatic parenchyma and CRLM. Lasers Surg. Med. 49:280–292, 2017. © 2016 The Authors.  CIconfidence intervalCLEconfocal laser endomicroscopyICGindocyanine greenpCLEprobe‐based confocal laser endomicroscopyCRLMcolorectal cancer liver metastasisROCreceiver operating characteristicsRFUrelative fluorescence unitGLIMMIXgeneralised linear mixed model analysis by SAS™SEstandard errorColorectal cancer is one of the most common cancers globally, being the third most commonly diagnosed cancer in men and second most common in women Surgical resection of solid tumour metastases is the preferred treatment for curative intent Unfortunately, visual assessment is insufficient to confirm complete tumour excision. Although intraoperative ultrasonography (IOUS) and bi‐manual palpation are frequently used to determine tumour location within the liver, this approach is not suitable to identify residual cancer following resection because it is mainly of a microscopic nature Other imaging modalities such as cone beam CT, magnetic resonance imaging and near infrared fluoroscopy may be able to enhance imaging during liver surgery, but none of these modalities provides an image resolution that would enable identification of residual disease at a microscopic level ex vivo, but only CLE has been extensively studied in an in vivo setting Optical imaging modalities offer micrometer scale image resolution but at this stage only optical coherence tomography (OCT) and confocal laser endomicroscopy (CLE) are available in compact designs that make an intraoperative application feasible. Either imaging modality can visualise liver tissue in vivo histology in real‐time Studies comparing confocal laser endomicroscopy (CLE) with standard histology in animal models, have proven its ability to visualise the cellular architecture of tissues, thereby, providing a method of obtaining virtual, To the best of our knowledge, the detection of liver metastasis using pCLE has not been investigated previously. Therefore, this study was conducted to assess to what extent dual wavelength pCLE imaging characteristics and quantitative fluorescence properties could discriminate between metastatic and normal liver tissue in an orthotopic nude mouse model of colorectal cancer liver metastases. Dual wavelength CLE imaging was achieved by recording measurements with the Cellvizio™ 488 and 660 nm system employing either fluorescein or indocyanine green as fluorophores, respectively. Both fluorophores are clinically licensed which enables findings to be translated into a clinical context without safety concerns.l‐glutamine and 10% v/v foetal bovine serum. All animal studies were conducted in accordance with UK home office regulations and the guidelines for the welfare and use of animals in cancer research. The liver metastasis model used has been previously described n = 8) MF1 nu/nu mice  underwent a laparotomy in order to directly inject 1 × 106 cells into the spleen. Splenectomy was performed after 5 minutes to stop formation of a primary tumour at the site of injection, with cells that had been carried by the blood flow through to the liver forming the solid tumours of the metastatic model. At conclusion of the procedure, the laparotomy incision was closed and animals were recovered. In the described animal model, female mice are exclusively used due to their better compliance.All animal studies were approved by the University College London Biological Services Ethical Review Committee and licensed under the UK Home Office regulations and the Guidance for the Operation of Animals (Scientific Procedures) Act 1986  and United Kingdom Co‐ordinating Committee on Cancer Research Guidelines for the Welfare and Use of Animals in Cancer Research Liver tumour maturation took between 5 and 6 weeks. The progression of metastatic disease was monitored once per week with non‐invasive magnetic resonance imaging under general anaesthesia. The evolving disease pattern showed a heterogeneous distribution of distinct solid tumour nodules throughout the lobes of the liver, typically comprising 10–20 tumour nodules that could be visualised on magnetic resonance imaging.A transverse laparotomy was carried out under general anaesthesia to allow unrestricted access to the liver. CLE imaging at 488 and 660 nm was performed with two different CLE imaging stacks with separate laser scanning units, probes and image processing computers. Both systems were initialised and calibrated at the start of each experiment. Switching between wavelengths simply required imaging probes to be swapped over. Due to the inability of pCLE to visualise tissues beyond 100 μm depth, images were exclusively acquired from superficial tissue, while the probes were manually maintained in contact with the liver surface. Motion artefact was reduced by gently applying the pCLE probes onto the liver surface, so that they moved in parallel with liver motion secondary to respiration.Images were acquired with the Z‐probe at 488 nm and the mini‐Z probe at 660 nm wavelength. Probe characteristics were the following: probe diameter 1.8 or 0.94 mm; lateral resolution 3.9 μm, axial resolution 44 or 13 μm, working distance 80 or 65 μm, maximal field of view 426 × 302 or 323 × 323μm for the Z‐probe or mini‐Z probe, respectively.The liver was macroscopically categorised into normal and cancerous tissue areas based on its respective brown or white appearance. This approach was made possible by the radically different appearance of normal liver versus CRLM. Image sequences that were disturbed by excessive motion artefact were not used, especially since they would frequently included images from normal and cancerous tissues at the same time. No attempt was made to identify microscopic disease because the aim was to establish reproducible CLE criteria of CRLM which could then be translated to the detection of microscopic disease in future studies. Baseline CLE images were recorded over a representative area prior to fluorophore administration. To provide exogenous fluorescence for CLE imaging at 488 and 660 nm excitation, fluorescein sodium  and ICG (molecular weight 774.96 g/mol) were mixed with 0.9% sodium chloride or 5% dextrose, respectively. Subsequently, fluorescein (4–7 mg/kg) and indocyanine green (0.3–0.5 mg/kg) were sequentially injected into the tail vein with a delay of 20–40 minutes between injections to allow adequate time for imaging. In preliminary experiments, it was confirmed that neither fluorescein nor ICG elicited unintentional “cross”‐fluorescence signals.An initial image sequence of 1–3 minutes was recorded to monitor and confirm distribution of the fluorophores in the liver. Several short sequences ranging from 10–20s were taken from normal and malignant tissue in different locations. The aim was to acquire images from as many tumour nodules, and an equivalent number of normal liver tissue areas, as possible. It was not feasible to co‐register tissue areas between 488 and 660 nm pCLE imaging. But due to the relatively small size of mouse livers, it was hypothesised that approximately, the same areas of view were imaged at both wavelengths. For statistical analysis which is described below, all values for individual animals were pooled into one group and hence co‐registration is less relevant than for the visual comparison of image features. Pooling of data was also intended to decrease the impact of heterogeneous fluorophore accumulation and tissue scattering properties within the same animal. The mean fluorescence values for each image frame from these sequences were subsequently analysed.in vivo examination.Following termination of the animals, the livers were resected, fixed in 10% formalin and embedded in paraffin blocks. Tissue samples were sliced at 4 μm thickness with a microtome and sections mounted on glass slides. The presence of colorectal cancer metastases was confirmed by using a standard structural Haematoxylin and Eosin stain. Because areas of normal liver and CRLM in this disease model looked radically different, histological examination enabled us to confirm infiltration of adenocarcinoma cells in representative liver areas which previously underwent CLE Cellvizio™ is packaged with image analysis software (Imagecell™) that records and displays basic values termed relative fluorescence units (RFU), from which the maximum, minimum, median and mean were calculated and exported for each image frame. Relative fluorescence units are based on a reconstruction of single fibre fluorescence intensity at a given point in time. A number of factors such as fibre injection rate, fibre collection rate, fibre auto‐fluorescence, biological sample fluorescence and laser source intensity influence fluorescence intensity. These variables are factors in an equation that allows the real time reconstruction of fluorescence intensity in individual fibres each of which corresponds to an image pixel of the CLE image. A detailed description of the equation and other relevant methodology has been previously published Negative RFU values either correspond to image noise or can be caused by a reduction in fibre autofluorescence due to prolonged illumination. To avoid misinterpretation, negative RFU values were thresholded at zero. All RFU values underwent a square root transformation to reduce data skew and kurtosis as previously reported Routine statistical comparison, e.g. by paired Wilcoxon rank sum test  was not possible due to specific constraints of the acquired data which were: (1) unequal number of frame counts for normal and CRLM tissue and (2) potential introduction of a repeat sampling error. These circumstances lead us to choose a generalised linear mixed model (GLIMMIX) analysis which can accommodate for the described data constraints by estimating the interaction of fixed and random effects. The GLIMMIX analysis enabled modelling of the probability of the acquired images corresponding to normal liver parenchyma. The measured RFU value and animal ID were set up as fixed effect and random effect for this model, respectively. A similar approach has previously been used by our group for CLE evaluation of ablated liver tissue in a porcine model Different estimation methods such as Pseudo Likelihood, Laplace and Gauss‐Hermite quadrature can be used for GLIMMIX analysis. For the purpose of this study, the Laplace method was chosen because it exhibited satisfactory convergence and the best fit characteristics as assessed by Akaike information criterion (AIC). GLIMMIX analysis results are given as the change in odds ratio for an increase in RFU at a standardised value. For example, an odds ratio of 2 at a standardised RFU increase of 100–101 means that the odds ratio of liver parenchyma being normal increases by 2 if the RFU value increased by one from 100 to 101.To test the validity of the GLIMMIX model resulting from the study data, a ROC analysis was performed. The covariates and intercepts provided by the GLIMMIX analysis were used to calculate the log odds, odds and finally, the probability of identifying normal liver tissue. Each single probability value corresponded to a single observation (mean RFU per frame), a specific animal ID and a tissue type which enabled us to perform a ROC analysis based on this data. Because animal ID was a random effect due to the difference in probe calibrations between experiments, the same RFU value would result in different probability values for individual animals.To determine if a probability based threshold value could be used to distinguish between normal and malignant tissue random sample cross validation was carried out. To this end, all observations were randomised into a training and a validation dataset. The training set was used to build a GLIMMIX prediction model whose output subsequently underwent ROC analysis. A threshold value with an optimal sensitivity and specificity for identifying normal liver tissue was then chosen from the ROC curve coordinates. This threshold probability value was subsequently applied to the validation set, to evaluate its performance in distinguishing between normal and malignant liver tissue and calculate the resulting sensitivity, specificity and accuracy for this test.An estimation of erythrocyte flow velocity calculated from the available image data was also carried out. To calculate flow velocity (in mm/s), the distance travelled by a single erythrocytes in the time period between two image frames (=41.7 ms at 24 frames per second) was measured. Depending on the amount of available data, 5–10 individual erythrocytes were assessed per tissue type and animal ID.P < 0.05. Statistical analysis was performed using SPSS™ Version 21  with exception of the GLIMMIX analysis which was carried out with SAS™ Version 9.4 . Quantitative data (RFU and erythrocyte flow velocity) is given as median and interquartile range. Odds ratios are stated together with their 95% confidence intervals (CI).Statistically significant differences were assumed if The orthotopic CRLM model was established in eight mice. One animal died before any CLE measurements could be carried out. The remaining seven mice underwent CLE examination. In two animals, the tumour burden relative to normal liver was too great to allow meaningful statistical comparison. Two animals died during image acquisition; one (ID6) before sufficient data at either wavelength could be collected and a further animal following completion of CLE imaging with fluorescein at 488 nm (ID7). In summary, six animals underwent consequential CLE imaging and of these, four (fluorescein/488 nm) and three (ICG/660 nm) experiments provided sufficient data for statistical analysis.Examples of CLE images and H&E histology for normal and CRLM tissue are shown below. Each example is from the same animal but due to the frequently small size of lesions, no attempt was made to spatially correlate CLE images with histological slides. Macroscopic lesion size was larger than the pCLE field of view but otherwise variable between animals. The smallest lesions were 1–2 mm whereas in some animals, the majority of the liver surface was covered by metastatic deposits.Prior to administration of fluorescein, no endogenous fluorescence was observed in normal or metastatic tissue. The initial period following fluorescein injection was termed the inflow phase. During this phase, the fluorescein strongly accumulated within the vascular compartment and could be observed in hepatic sinusoids and interlobular vessels Fig. a. OccasiInflow phase duration varied between animals but was approximately 7–11 minutes. At the end of the inflow phase, the fluorophore shifted from the intravascular compartment to the liver parenchyma and connective tissue, which resulted in high signal intensity within liver lobules. This stage of the CLE examination, lasting throughout the remainder of observations, was termed the parenchymal phase. Throughout its duration, cords of hepatocytes and the sinusoid structure could frequently be observed Fig. a. SinusoDifferentiation between types of vasculature  was challenging at either imaging phase but could sometimes be accomplished by interpreting the vessel's location and morphology. For example, a central vein (which drains towards hepatic vein) could be identified by its location and orientation within the middle of a liver lobule Fig. b.Areas of CRLM could be analysed in detail at 488 nm wavelength using fluorescein. The majority of malignant tissue showed a pattern of dark patches criss‐crossed by bright linear structures. The areas devoid of fluorescence represented malignant tissue which was interspersed by torturous, neoangiogenic vessels that traversed throughout the metastasis. Fluorescein appeared to be retained within tumour vessels for longer than in the healthy hepatic vasculature. This resulted in neoplastic vessels displaying high intensity fluorescence throughout most of the image acquisition. Infrequently, glandular structures typical of moderate to well differentiated colonic malignancy could be visualised Fig. a.Visible erythrocytes exhibited a mostly slow and erratic movement when compared to normal hepatic vessels Fig. b and c. CLE examination at 660 nm did not reveal any endogenous fluorescence in areas of normal liver or CRLM tissue prior to ICG injection. In contrast to CLE imaging at 488 nm with fluorescein, no signal was detectable from blood vessels during the inflow phase following ICG administration, and erythrocyte movement could not be visualised. In the parenchymal phase, however, structures with the configuration of central lobular veins could be identified . This was also true for the ICG/660 nm combination where a RFU value change from 10.5 to 11.5 resulted in an odds ratio increase of 4.6 . The impact of treating individual animals as a random effect did significantly contribute to the fit of either model with a covariance parameter of 3.9 ± 2.8  for fluorescein/488 nm and 43.0 ± 35.8  for ICG/660 nm. The covariance parameter estimation indicates to what degree the random variable affects the fit of the mathematical model (e.g. aids in predicting outcome) and its associated standard error gives a measure of repeat sampling variability.For the fluorescein/488 nm combination, a RFU value change from 37.8 to 38.8 increased the odds ratio of liver tissue being normal (e.g. nonmetastatic) by 1.2  and 5.08  for the fluorescein/488 nm and ICG/660 nm combinations, respectively. Again the random effect from performing measurements in different animals did contribute to the fit of the model with a covariance parameter of 3.8 ± 2.7  for fluorescein/488 nm and 49.2 ± 42.0  for ICG/660 nm.Subsequently, observations were randomised into a training and validation set for each fluorophore and wavelength combination. GLIMMIX analysis of the training samples retained statistical significance with a RFU increase from 37.7 to 38.7 and 10.6 to 11.6 resulting in an odds ratio increase of 1.22  for fluorescein/488 nm and 0.994 ± 0.001 (CI 0.992–0.997) for ICG/660 nm. A probability threshold value of 0.67 for fluorescein/488 nm and 0.69 for ICG/660 nm  with a high corresponding sensitivity and specificity was chosen from the ROC curve coordinates. Based on these threshold values, the validation set observations were categorised into either normal or metastatic tissue which consequently enabled calculation of sensitivity and specificity for the proposed quantification method. For the fluorescein/488 nm combination, sensitivity and specificity was 82.9% (CI 81.1–84.6%) and 85.2% (CI 83.2–87.0%) respectively. For CLE imaging of ICG at 660 nm wavelength, the sensitivity and specificity was 97.9% (CI 97.0–98.6%) and 97.5% (CI 96.4–98.4%), respectively.in vivo comparison of dual‐wavelength CLE imaging in combination with two exogenous fluorescent probes as a method of differentiating liver metastases from normal liver parenchyma. The CLE system used in this study allows high‐resolution (3.9 µm), imaging of fluorescence with a field of view of 424 × 302 µm (488 nm) or 323 × 323 µm (660 nm), thereby, allowing in vivo tissue structure and function evaluation in real time.This article represents the first Two distinct phases of CLE liver imaging, based on fluorophore behaviour, have been described. The inflow phase lasted up to 11 minutes and was characterised by accumulation of fluorophore  in the intravascular space. Following on seamlessly, and lasting for the remainder of the CLE examination, the parenchymal phase showed a signal intensity shift to the liver lobules. Images obtained in healthy liver tissue at 488 nm wavelength with fluorescein provided good detail of the sinusoidal structure and hepatic vascular architecture. In contrast, CRLM tissue showed a pattern of tumour cell agglomerations which were traversed by haphazardly arranged blood vessels.Quantitative estimation revealed that erythrocyte flow velocity in CRLM tissue was slower than in normal liver tissue. In contrast to healthy vessels that relatively quickly lost fluorescence during the inflow phase, the signal intensity in tumour vasculature remained high throughout the inflow and parenchymal phases of imaging. This was intriguing because, commonly, tumour vessels are reported as having increased permeability Imaging at 660 nm wavelength with ICG was dominated by the rapid uptake of ICG into the cytoplasm of normal hepatocytes. Visualising the whole field of view enabled sinusoid architecture to be identified, whereas on an intracellular level, hepatocyte nuclei could be observed as dark areas contrasted by the strong cytoplasmatic fluorescence signal. Localised regions of high intensity fluorescence that were seen at the later stage of imaging could not reliably be attributed to a specific feature of liver histology. Because ICG is known to be exclusively excreted by bile Areas of CRLM were characterised by lack of ICG mediated fluorescence purportedly due to the lack of ICG uptake in the cancerous tissue. The absence of any relevant imaging signal prevented the characterisation of CRLM tissue with this wavelength and fluorophore combination. The general finding was that fluorescein in combination with CLE at 488 nm was more suited to the imaging of the vasculature and for examining CRLM morphology, which was characterised by a mosaic of dark areas, irregular vasculature and, occasionally, glandular structures. In contrast, CLE imaging of ICG at 660 nm appeared to be better suited to assess the structure of sinusoids and the surrounding hepatocytes.in vivo CLE examination of human liver disease have been published to date Although some reports on The feasibility of using CLE intraoperatively to confirm complete removal of cancer in the resection of intracranial malignancy has been demonstrated previously The same rationale can also be applied to ablation therapy of CRLM which is emerging as a feasible alternative or auxiliary treatment modality beside surgical resection. In liver ablation, cancerous tissue is destroyed by inducing coagulation necrosis through a variety of methods  In the current study, it was demonstrated that utilising a dual wavelength approach may be more advantageous than using single wavelength CLE Other groups have explored the potential of CLE imaging for identifying cancers and evaluated these in animal models Previous articles on CLE imaging of malignancy have been based on subjective, observer dependent image interpretation but have not identified quantifiable and reproducible parameters of malignancy Because probability values used for ROC analysis are based on a combination of a fixed (RFU) and a random effect , it cannot be extrapolated into a specific RFU value for future studies. Before this can be considered, it is crucial to standardise RFU value calibration which could potentially result in reproducible and absolute fluorescence units that could be globally applied across different research groups.Further limitations that have to be taken into account regarding the presented findings pertain to the animal model and the technical characteristics of confocal laser microscopy. The animal model of CRLM that was studied uses a human cancer cell line and a portal venous route of establishing liver metastasis which is the most common route of GI malignancy disseminating to the liver in humans A clear trend for lower fluorescence intensity in CRLM compared to healthy liver tissue was especially prominent on CLE imaging of ICG fluorescence, probably, because it has a strong affinity for hepatic tissue. Colorectal cancer liver metastasis is a common indication for surgical resection of liver malignancy in the western hemisphere and is generally regarded as the only curative treatment option for this disease 2 which may limit its applicability in the clinical examination of liver resection margins which are usually in the cm2‐range. It has previously been shown, however, that clinically relevant pCLE imaging with a field of view of approximately 2 mm22, because this would greatly improve the integration of pCLE imaging into the surgical workflow.Further restrictions that have to be accounted for are related to the technology behind pCLE imaging. Optical imaging modalities including CLE can visualise details down to a sub‐cellular level but at the cost of a limited imaging depth. The maximal imaging depth of the Cellvizio™ probes used by our group was 0–70 μm depending on probe type, which means that only liver cell architecture that is either superficial or adjacent to the resection margin could be assessed. It has been shown that removal of tumour within an area of <1 mm conveys a patient survival benefit in the resection of CRLM Currently, Cellvizio™ probes are only marketed for endoscopic applications and are not certified for intraoperative use. Some of the probes, however, can be fully sterilised and may potentially be used for imaging during laparoscopic liver resection. If a clinical benefit for the intraoperative usage of this system can be defined, sterility issues and clinical re‐certification should not present a major obstacle.In conclusion, a clinically licensed pCLE system was used to provide a detailed description of discriminatory tissue characteristics in an orthotopic murine model of CRLM. A dual wavelength approach in conjunction with two fluorophores was found to be of benefit because CLE imaging of fluorescein at 488 nm enabled better visualisation of metastatic tissue whereas quantification of ICG mediated fluorescence intensity demonstrated a better potential to objectively discriminate between normal liver and CRLM tissue at a cellular level. Evaluation of simultaneous  dual waveband imaging and standardisation of fluorescence values is the next crucial step to advance pCLE imaging of liver malignancy.


In this work, MOSFET detectors with brass buildup caps placed on the patient's skin surface on the central axis of the individual IMRT beams are used to determine the maximum entrance dose (Dmax) from the prostate IMRT fields. A general formalism with various correction factors taken into account to predict Dmax entrance dose for the IMRT fields with MOSFETs was developed and compared against predicted dose from the treatment‐planning system (TPS). We achieved an overall accuracy of better than ±5% on all measured fields for both 6‐MV and 10‐MV beams when compared to predicted doses from the Philips PinnacleThe feasibility of using dual bias metal oxide semiconductor field effect transistor (MOSFET) detectors with the new hemispherical brass buildup cap for PACS numbers: 87.53Xd, 87.56Fc Metal oxide semiconductor field effect transistor (MOSFET) dosimeters with plastic water buildup caps have been in use in our institution for some time to verify routine patient entrance Dmax doses, and we have had excellent agreement with predicted doses as reported by other groups.(–) More recently, Thomson Nielsen, the manufacturer of MOSFETs, has introduced single, wide‐energy hemispherical buildup caps for patient dosimetry that have full buildup at Dmax for all photon energies.Routine intensity‐modulated radiotherapy (IMRT) quality assurance in most institutions only involves verifying the optimized fluence map delivered to the patient in a test phantom at a certain preset depth. Reconstruction of actual patient dose delivered to the patient through electronic portal imaging systems is still in its infancy. In this scenario, verification of actual dose delivered to the patient through r groups.–3 MoDmax entrance dose for anterior IMRT fieldsWe have developed a general formalism with all the correction factors taken into account to predict 1 cGy/mV, and for high sensitivity conversion factors are around 3 cGy/mV. The system is powered by a 9‐V battery (two batteries are required if using the high‐sensitivity mode).We used the MOSFET AutoSense dose verification system, model TN‐RD‐60‐W10, from Thomson Nielsen  for this study. The system consists of dual bias MOSFETs, model TN‐502‐RD, and a dual bias supply box, model TN‐RD‐22  In our clinical practice, we currently use a dual P‐channel enhancement mode type MOSFET.The physical characteristics of MOSFETs and the principles of dual bias detector operation are described in great detail elsewhere.,,4 The dual MOSFET used in this study consists of two MOSFETs on a silicon layer with different bias voltages applied to the gate. The dose measured in this case is then the ratio of the voltage shifts for the two MOSFETs. This is an advantage over single MOSFETs, which suffer from temperature and dose rate dependence. These effects are normalized in the dual MOSFET, and they essentially show no temperature effects as reported elsewhere.,5(8.5 g/cm3) and high atomic number, brass provides the minimal amount of metal needed to achieve full buildup at Dmax for a range of photon energies. The buildup caps are small, with a radius of only 0.635 cm. They have a special groove insert for MOSFET placement, which, we believe, improves placement accuracy and saves on patient setup time  of the seven fields. We achieved an overall accuracy greater than or equal to ±5% on all measured fields. We report the data here for the first 11 patients in our institution.Our prostate IMRT delivery method involves a step‐and‐shoot technique using a Varian 21 EX linear accelerator and seven equally spaced beams starting with the AP beam. in vivo IMRT measurement is given by expected MOSFET dose (cGy) =The general equation to calculate expected dose for where ISF is the inverse square factor, which corrects for the changes in the source‐to‐skin distance (SSD) from the planned IMRT treatment plan to the actual SSD on the day MOSFET was placed. The other correction factors are described in detail below.Dmax entrance skin dose from prostate IMRT beams with various correction factors .We now present a general formalism to predict for 1 mV/cGy for standard sensitivity and 3 mV/cGy for high sensitivity. All our measurements were performed under high sensitivity.Each new set of MOSFETs must be calibrated to determine the device's conversion factor (CF) value. This provides the user with the millivolt (mV) to centigray (cGy) conversion. Typical values are approximately Dmax depth under full buildup (bolus and solid water measuring 30×30 cm2) at 100 cm SSD and field size of 10×10 cm2.We performed our calibration under full‐scatter conditions for the 10 MV beam; that is, the MOSFETs are placed at a ±3% over a two‐week period.Five measurements were made with each MOSFET after delivering 50 cGy . The standard deviation of the detector ranged from 0.3% to 1.3%. The AutoSense system allows the user to store these conversion factors in the software program. It should also be noted that the response of MOSFETs changes over time as reported by Chuang et al.,(CR=1.0), while the measurements with the 10‐MV beams were calibrated under a full‐scatter environment without the brass buildup caps.An alternative method of generating CF values is to perform the calibration with the buildup caps. This is more time‐consuming because each MOSFET has to be calibrated individually. A CF value measured this way is specific to the beam energy used for the calibration as well as the type of cap used. The advantage of this method is that the additional correction factor (CR) described below is built into the CF value. Our IMRT measurements using 6‐MV beams were calibrated this way 100 cm+Dmax by delivering the same known dose to both under standard reference conditions:CR values correct for the scatter environment changes when buildup caps are used when the original calibration was performed under full‐scatter conditions without the brass buildup caps. Mathematically, the CR value is the dose measured by the MOSFET under buildup cap at 100 cm SSD divided by the dose measured by the chamber at SSD+Dmax), and the changes in the scatter environment due to the electron density change with the presence of the brass buildup cap. The reference dose with chamber was measured using the Exradin A1 miniature shonka thimbe chamber connected to a CNMC model 206 electrometer.A CR value measured this way incorporates small corrections needed in the inverse square effect . Fifty centigrays was delivered each time with a field size of 10×10 cm2.We investigated whether the responses of MOSFET detectors are dose rate independent by taking readings at three different SSDs  at two different dose rates Our results indicated a difference of less than 1% for the two different dose rates; hence, the responses of MOSFETS are essentially dose rate independent.10×10 cm2 field size. The MOSFET readings were averaged, and then we took the ratio of MOSFET to chamber dose and normalized the data to 100 cm SSD.Two MOSFETs were used at each SSD, each taking three readings. We measured data over the range of 75 cm to 120 cm SSD with the MOSFETs covered with the brass buildup cap. From 75 cm to 100 cm SSD, we measured in 5‐cm increments. Three measurements with an ion chamber were also taken at each SSD. Fifty centigrays was delivered for all measurements. All readings were performed in a As illustrated in Fig. in vivo IMRT measurements involves the computation of the IMRT patient‐specific correction factor (CFIMRT) for each individual IMRT field. The CFIMRT was derived by transferring the patient treatment plan to the solid water phantom by the treatment‐planning system (TPS), then the measurements were made on the phantom. The CFIMRT factor is defined as the ratio of dose measured by MOSFET with the brass buildup cap to the dose measured by the chamber at Dmax. These measurements were all performed at a nominal gantry angle of 180± (AP) for each of the IMRT beams.The final step in deriving the expected dose for CFIMRT represent the ratio of dose measured with MOSFET to the dose measured with ion chamber, and since the CR value represents the correction factor more suitable for standard conditions , the CR value is factored out in the computation of IMRT patient‐specific correction factor to ensure that this factor is not counted twice. For 6‐MV beams, since the original calibration was done with buildup caps, the CR value is 1.0, as discussed before.It should be noted that since both the CR value and the Dmax for small field sizes and leaf sequences encountered in IMRT delivery when compared to the predicted dose from the TPSs. We also verified the dose predicted at Dmax by our TPS  for the first four patients, where the individual field dose varied from 26.3 cGy to 78.5 cGy with independent chamber measurements with the Exradin A1 miniature shonka thimble chamber, which has a collecting volume of 0.056 cm3. Our measurements agreed with the TPS calculated dose within 2% or 1 cGy. Consequently, for all future in vivo measurements, dose from the TPS was used as a reference, and the IMRT patient specific factor is defined asThe IMRT correction factor represents a perturbation in MOSFET measurements at CFIMRT, since it would involve delivering the patient plan independently twice to calculate the MOSFET dose and the chamber dose. The user must verify that the dose predicted at Dmax by the TPS agrees reasonably well with chamber measurements before using dose at Dmax from the TPS as a reference in the computation of the patient‐specific IMRT correction factor.This was done mainly to avoid the extremely time‐consuming computation of in vivo dosimetry data using 10‐MV photons, which used full buildup calibration and the CMS XiO TPS, and data in Table in vivo dosimetry using 6‐MV photons and the Philips PinnacleWe present the data in two tables. Table ±3%), errors in positioning of the brass buildup cap, patient movement, and the fact that the patient‐specific IMRT correction factor was derived with a nominal gantry angle of 180±, which is different from the actual treatment gantry angle.From Tables Our clinical data are still well within the overall uncertainty in the calculation of MOSFET dose for IMRT therapy. We estimate the total uncertainty in the estimation of the MOSFET dose in the high‐sensitivity mode for IMRT therapy to be 4.6 %, as described in Table Our standard deviations in the measurement of the SSD correction factor and the CR value based on raw data measurements are different than those reported by Jornet et al.in vivo prostate IMRT dosimetry was investigated and achieved. The new brass buildup caps, because of their small size and groove design, improve accuracy in positioning and take less patient setup time. We have derived a general formalism with a patient‐specific IMRT correction factor (CFIMRT) to predict expected Dmax entrance dose using these brass buildup caps for anterior IMRT fields. We have achieved an overall accuracy of better than ±5% on all measured fields for both 6‐MV and 10‐MV beams when compared to predicted doses from the Philips PinnacleThe feasibility of using new brass buildup caps for in vivo dosimetry along with regular fluence map verification in phantoms at a preset depth using films or diode array and routine multileaf collimator quality assurance offers a greater accuracy and confidence in actual dose delivered to the patient.We believe that integration of One of us (JM) would like to thank Dr. John Broadhurst, Dr. Pat Higgins, Dr. Russ Ritenour, and Dr. Bruce Gerbi, faculty members at University of Minnesota, for their help and support.


Understanding the difficulties faced by different migrant groups is vital to address disparities and inform targeted health‐care service delivery. Migrant oncology patients experience increased morbidity, mortality and psychological distress, with this tentatively linked to language and communication difficulties. The objective of this exploratory study was to investigate the communication barriers and challenges experienced by Arabic, Greek and Chinese (Mandarin and Cantonese) speaking oncology patients in Australia.This study employed a cross‐sectional design using patient‐reported outcome survey data from migrant and English‐speaking Australian‐born patients with cancer. Patients were recruited through oncology clinics and Australian state cancer registries. Data were collected regarding patient clinical and demographic characteristics and health‐care and communication experiences. Data from the clinics and registries were combined for analysis.Significant differences were found between migrant groups in demographic characteristics, communication and health‐care experiences, and information and care preferences. Chinese patients cited problems with understanding medical information, the Australian health‐care system, and communicating with their health‐care team. Conversely, Arabic‐ and Greek‐speaking patients reported higher understanding of the health‐care system, and less communication difficulties.Our study findings suggest that migrant groups differ from each other in their health communication expectations and requirements. Lower education and health literacy of some groups may play a role in poorer health outcomes. Public health interventions and assistance provided to migrants should be tailored to the specific needs and characteristics of that language or cultural group. Future research directions are discussed. Migrant groups are likely to differ from each other on many dimensions, such as education and literacy, income and access to insurance, exposure to trauma and time spent in the new home country. It is well established through epidemiological research that there are differences in cancer morbidity and mortality between migrant language and cultural or location groupings due to environmental and behavioural influences on the aetiology of various cancers.Understanding the difficulties faced by different migrant groups is therefore vital to inform targeted, evidence‐based policies and service delivery in health care.2This study utilized data from two previous studies conducted as part of a research programme investigating patient‐reported outcomes and experiences of migrant and English‐speaking Australian‐born patients with cancer. For the first study, potential respondents were identified via the Australian state Cancer Registries in New South Wales, Queensland and Victoria.2.1A total of 1441 patients were recruited: 596 patients via the state cancer registries and 845 patients via oncology clinics with a response rate of 26% for patients recruited via cancer registries and 62% for patients recruited via oncology clinics.2.2Arabic, Greek and Chinese community advisory groups reviewed study procedures and materials, and provided advice regarding recruitment strategies and interpretation of results. A total of 2307 patients were identified as eligible in the registry study, and 693 of these patients consented, via telephone, to be mailed the survey. Out of a total 1603 potentially eligible patients identified from clinic lists, 1603 patients were approached by staff in outpatient clinics and a total of 1603 patients consented to be mailed a survey. Upon consent, participants in both studies were posted a questionnaire in their primary language. Non‐returnees were telephoned up to three times at different times of the day before being listed as non‐respondents.2.3All measures were translated into Arabic, Greek and Chinese using the European Organisation for Research and Treatment of Cancer Translation Protocol.2.3.1Patient‐reported demographic and clinical details were collected including age at participation, age at diagnosis, date of diagnosis, primary cancer type, stage of disease at diagnosis, years lived in Australia, marital status, employment status and education level. For the cancer registry study, demographic and clinical details were obtained directly from the registry, including age at diagnosis, date of diagnosis, primary cancer type, disease extent and diagnosis, and measures of rurality and socio‐economic status based on postcode at diagnosis. A measure of socio‐economic status was calculated using the Index of Relative Socio‐economic Advantage and Disadvantage (IRSAD), which indicates relatively greater disadvantage (low scores) or relative lack of disadvantage (high scores) based on the economic and social conditions of people within a geographical area.2.3.2Patients’ experiences of communication within the health‐care system and their understanding of the health‐care system were assessed with custom measures. Purpose‐built measures were used as there were no existing scales measuring immigrant‐specific communication variables. The items were developed, piloted on a small sample, and then implemented in the current studies.Participants rated their understanding of the Australian health system and their understanding of aspects of their illness and care on four‐point and five‐point Likert scales  for all relevant tests. No adjustment was made for multiple testing, as this was an exploratory study with no pre‐specified hypotheses.33.1Demographic and clinical characteristics by language group are provided in Table There were no statistically significant differences between language groups in disease stage, whether currently on treatment, or months since diagnosis. However, there were significant differences between language groups in cancer type: specifically, fewer Greek‐speaking participants had a diagnosis of breast cancer, more Chinese‐speaking patients were diagnosed with lung cancer and fewer with prostate cancer, and English‐speaking Australian‐born patients had more than expected diagnosis of prostate, bladder, kidney, and lung cancers.Significantly more Greek‐speaking respondents were male (56% vs 42%‐46% in the other language groups). More Chinese‐speaking and fewer English‐speaking Australian‐born respondents were married or partnered (79% vs 71%). Chinese‐speaking respondents were the most highly educated group, with 37% indicating that they had completed a university degree. In contrast, English‐speaking Australian‐born respondents were more likely to indicate that they had completed high school or technical college (73%) and Greek‐speaking respondents that they had not completed high school (54%). Approximately half of the Arabic‐speaking respondents had completed high school or technical college (56%); however, there was a fairly even split between Arabic‐speaking patients who had not completed high school education and who had completed university, 22% and 23% respectively.Age, IRSAD, age at diagnosis, and years lived in Australia also differed significantly between language groups. Chinese‐speaking respondents were significantly younger and Greek‐speaking respondents were significantly older than other groups, both in general and at age of diagnosis. Arabic‐speaking patients scored significantly lower on the IRSAD, indicating relatively greater socio‐economic disadvantage, whereas Chinese‐speaking patients scored significantly higher, indicating relatively greater socio‐economic advantage. Greek‐speaking respondents had lived in Australia the longest of all the migrant groups and Chinese‐speaking the shortest.3.2The relationship between language groups and health‐care understanding, perceptions, and experiences are provided in Table Significantly fewer Chinese‐speaking respondents and more English‐speaking Australian‐born respondents reported understanding the health‐care system, reasons for tests, and possible treatment effects on their body (see Table too much information (22%) than in the other language groups (5%‐13%). Conversely, more Chinese‐speaking respondents felt that they had been given insufficient information (14%).More English‐speaking Australian‐born respondents and fewer Greek‐speaking respondents felt they had been given the right amount of verbal or written information (88% and 68% respectively). More Greek‐speaking respondents felt that they had been given 3.3Responses to items assessing confidence with English, professional interpreter services, and perceptions of quality of care by language group are provided in Table Significantly more Chinese‐speaking respondents reported difficulties communicating with doctors in English (75% vs 61% and 63% for Greek‐ and Arabic‐speaking respondents respectively). Additionally, when reviewing responses from oncology clinic participants alone, associations between language group and confidence speaking and understanding English were significant. Fewer Chinese‐speaking respondents were confident speaking (45%) and understanding (44%) English when communicating with hospital staff other than doctors or nurses compared with Arabic‐ and Greek‐speaking respondents. Conversely, associations between language group and confidence in English were not significant for registry participants, possibly due to the wording of the question for this sample.More Chinese‐speaking and fewer Greek‐speaking respondents reported that they needed a professional to interpret at medical visits “sometimes” or “often” (56% vs 44%); however, the wording of the question may have excluded some patients who used a family member for interpretation. Language group was not associated with perceptions of interpretation quality. The majority of respondents felt that they were confident with the accuracy of their interpreter (89‐92%) and explanations of medical terminologies (86‐93%) and felt comfortable with the interpreter (85%‐91%).Among respondents who self‐assessed as not speaking English well, the majority felt that they had received “the same” or “better” care regardless of their cultural background (97%‐99%). However, significantly more Chinese‐speaking respondents believed that the quality of care they received was worse because of their lack of English (15% vs 9% and 7% for Arabic‐ and Greek‐speaking respondents respectively).4This study aimed to explore and identify experiences of communication within the health‐care system of migrant groups diagnosed with cancer in Australia, by looking at some of the most prevalent language groups in the country. Our findings demonstrated that Arabic‐, Greek‐ and Chinese‐speaking patients differ from each other in terms of demographic and clinical characteristics, their experience of communication within the health‐care setting and their understanding of the health‐care system. Evidence of these differences allows for identification and implementation of more targeted health‐care improvements and outcomes for migrant groups in the oncology setting.4.1The findings from our exploratory study suggest what may be the typical clinical and demographic characteristics of certain migrant cancer patient groups in Australia. Our data were combined from two different studies employing two different sampling methods, so our sample is likely to be a fair representation of the Arabic, Greek and Chinese migrant populations with cancer in Australia. Significant group differences in age, sex, education, and socio‐economic status arguably reflect different situations, times, and reasons for migration to Australia. Many people migrated from Greece to Australia after World War II. Greek migrants in the sample were predominantly male and less likely to have completed formal education, with just over half not completing high school. Greek patients also were older at age of diagnosis, and as a cohort in general. Many Greek migrants had lived in Australia for the majority of their lives, almost double the average years in Australia of the Arabic and Chinese cohorts. The Civil War in Lebanon from 1975 to 1990s led to the migration of many Arabic‐speaking people, and migration has continued from many Arabic‐speaking nations leading to great diversity in the Arabic‐speaking community in Australia.Understanding of demographic trends within specific migrant groups, and differences between groups, can have implications for health promotion and prevention strategies and interventions to improve health‐care delivery and health outcomes. Some examples are as follows: significant differences in sex prevalence within a migrant group could inform increased information and awareness around screening for male/female‐specific cancers; knowledge of lower education in certain groups could lead to targeted health literacy interventions in specific migrant populations; and knowledge of lower socio‐economic status may flag other risk factors for particular migrant groups. Additionally, identification of demographic trends in particular migrant groups can highlight the cultural differences between groups that should be considered in health‐care delivery, for example: the relationship between ethnicity, smoking, and oral cancer;4.2These results are consistent with the results of previous literature which found that limited English proficiency was related to lower levels of medical comprehension and reduced communication of important medical information.The disparity between English‐speaking Australian‐born and Chinese‐speaking patients in both their understanding of the health‐care system and confidence in communication is consistent with previous literature.The traditional Chinese medical paradigm is notably different to that of Western medicine.Importantly, the Arabic‐ and Greek‐speaking patients in our study reported quite a high understanding of the health‐care system , which contrasts with our findings within the Chinese‐speaking patients mentioned above, as well as with the literature that reports low understanding of the health‐care system in migrants in general. Variation in reported understanding of the health‐care system may be a reflection of time lived in Australia . However, this explanation does not account for Arabic‐speaking patients as there is a wide variation in length of time spent in Australia in this cohort. Encouragingly, the majority of patients felt that their care was not affected by a lack of English skills, except a small but noteworthy minority of Chinese‐speaking patients. Further investigation would be beneficial to better understand and provide more appropriate assistance for these patients.4.3Greek‐speaking patients, who as a cohort had significantly lower reported education levels, were more likely to report being given too much information, while Chinese‐speaking migrants, whose cohort reported significantly higher education levels, were more likely to report being given too little information. Conversely, other research has found that for English‐speaking patients, low education is linked to a preference for more information.4.4Previous research has indicated that communication difficulties may result in higher levels of anxiety and psychological distress in migrants.4.5Strengths of this study include a large sample recruited from a broad variety of sources. The study assessed migrants as separate groups according to both ethnic background and language spoken. Consumer reference groups for each migrant group reviewed study design and processes for cultural appropriateness.Last name identification for identifying non‐English‐speaking patients is a potential limitation of the study due to possible inaccuracies. However, patients were asked to verify ethnic background and language spoken, and were excluded if they did not confirm their language group. Another potential limitation is that patients were classified by language rather than by country of origin. This is particularly pertinent for the Arabic‐speaking cohort of patients, which represents a combination of migrants from many different countries and cultures. Further, the many Chinese dialects, the most prominent being Mandarin and Cantonese, were classified together. There were also minor differences in the wording of some items between the registry and outpatient samples. Different outcomes between the language groups may have been due to this methodological anomaly rather than experiential or cultural differences in communication and health‐care knowledge. Measures also did not undergo psychometric testing. Finally, no adjustment was made for multiple testing, as this is not strictly required in exploratory studies. Results should therefore be interpreted cautiously until examined in a confirmatory study.5The results of this study suggest that migrant language groups differ in terms of the issues they face and their health communication requirements. Future research should attempt to address and understand the differences between and within migrant groups in order to assist in the optimal allocation of resources to create targeted health‐care education, screening and health service delivery for people with cancer.No conflict of interest relevant to the study. David Goldstein: Bayer, Glaxo, Pfizer (C/A); Celgene . The other authors indicated no financial relationships.


Treatment decisions by CHWs were validated by microscopy on a reference blood slide collected at the time of consultation, to compare the proportion of children <5 years receiving appropriately targeted ACT treatment, defined as patients with microscopically‐confirmed presence of parasites in a peripheral blood smear receiving artemether‐lumefantrine or rectal artesunate, and patients with no malaria parasites not given ACT.Cluster‐randomized trials were conducted in two contrasting areas of moderate‐to‐high and low malaria transmission in rural Uganda. Each trial examined the effectiveness of ACT treatment was appropriately targeted in 79.3% (520/656) of children seen by CHWs using mRDTs to diagnose malaria, vs. 30.8% (215/699) of children seen by CHWs using presumptive diagnosis (P < 0.001). In the low transmission area, 90.1% (363/403) children seen by CHWs using mRDTs received appropriately targeted ACT treatment vs. 7.8% (64/817) seen by CHWs using presumptive diagnosis (P < 0.001). Low mRDT sensitivity in children with low‐density parasitaemia (<200 parasites/μl) was identified as a potential concern.In the moderate‐to‐high transmission area, mRDTs, ACT treatments delivered by CHWs are more accurately targeted to children with malaria parasites. mRDT use could play an important role in reducing overdiagnosis of malaria and improving fever case management within iCCM, in both moderate‐to‐high and low transmission areas. Nonetheless, missed treatments due to the low sensitivity of current mRDTs in patients with low parasite density are a concern. For community‐based treatment in areas of low transmission and/or non‐immune populations, presumptive treatment of all fevers as malaria may be advisable, until more sensitive diagnostic assays, suitable for routine use by CHWs in remote settings, become available.When equipped with  The study took place in two different malaria transmission settings in rural Uganda to provide evidence to optimise use of ACTs and mRDTs within programmes of integrated community case management (iCCM) that are currently being scaled up in Uganda and other countries.Early diagnosis and effective case management is a central component of the current malaria control strategy The study was conducted in Rukungiri District, south‐western Uganda, inhabited by the Bahororo and Bakiga ethnic groups, whose main occupation is subsistence farming. Anaemia among under‐fives in south‐western Uganda is estimated at 40%, and in other regions of the country, it ranges from 57% to 74% All villages in the two subcounties were invited to participate in the trial: 63 villages in Bwambara, ranging between 981–1203 m, and 64 villages in Nyakishenyi, ranging between 1064–2157 m. A public meeting was held in each eligible community to explain the purpose of the research, and community members were asked to identify 2–3 CHWs per village for training. Written informed consent to take part in the trial was sought from community leaders and all CHWs selected for training, of whom more than 50% had previously worked in the home‐based management of fever (HBMF) programme that was defunct at the time of the study.A cluster‐randomized two‐arm trial design was used to compare the effectiveness of mRDTs used by CHWs to diagnose and treat malaria with ACT [intervention] to presumptive diagnosis and treatment of malaria with ACT [control]. The unit of randomisation was the village, with all CHWs in the same village receiving the same type of training. Within each transmission area (subcounty), villages were randomly allocated, using a random number table in Epi Info, to either the intervention (mRDT) or control (presumptive treatment) arm.Community health workers in both arms of the trial were trained in recognising signs and symptoms of malaria, how to administer antimalarial treatment and when to refer Figure , Table 1www.actconsortium.org/RDThomemanagement). Training workshops lasted 3 days for CHWs in the control arm and 4 days for CHWs in the mRDT intervention arm. The latter received an additional 1‐day training to cover the rationale for diagnostic testing in febrile patients, performing an mRDT and interpretation of the test result based on training materials from WHO Participatory training workshops in malaria case management were held for CHWs in each arm, based on a trainer's manual and accompanying set of pictorial job aids . The key messages were that not all fevers are malaria and hence a diagnostic test was advisable before treatment with ACT; and a quick test (mRDT) could test for malaria, and these tests were available from government health facilities and trained CHWs in the intervention villages.For the first 6 months of implementation (July–December 2010), CHWs in both arms received additional close support supervision by project staff through parish‐level CHW meetings to assist adoption of the new procedures into everyday practice, to promote accurate and complete record keeping and to give advice on how to handle difficult situations that could affect adherence, for example caretakers insisting on treatment even after a negative RDT result. At the end of the 6 months, supervision was scaled back to periodic contacts when CHWs came to collect their supplies and a monthly allowance for paraffin and soap intended to support good hygiene and work in the evening/night . CHWs were also provided with pictorial job aids, training certificates, bicycles and T‐shirts at the start of the programme. Although CHWs in Uganda are volunteers, expected to work for free, programmes often provide small ‘refunds’ or tokens, which are generally expected as compensation.Data on diagnosis and treatment were recorded prospectively on all children seeking treatment for fever in carbon‐copy duplicated CHWs' registers, provided for this purpose. The treatment register for CHWs in the control arm was similar to the intervention arm except for data on mRDT result. CHWs in both arms were trained on how to prepare a thick blood film for every child with fever who consulted them, for later microscopy, subject to verbal assent given by the caretaker accompanying the child. Treatment decisions made by CHWs were validated by light microscopy on the reference blood slide collected at the time of consultation, to compare appropriate targeting of ACTs between arms over a 12‐month period (January–December 2011). The primary endpoint was the proportion of febrile children with malaria receiving appropriately targeted treatment with a first‐line antimalarial, a composite outcome defined as: children with microscopically‐confirmed presence of parasites in a peripheral blood smear (slide positive) who received either artemether–lumefantrine or rectal artesunate, and febrile children with no malaria parasites (slide negative) who did not receive either of these two artemisin‐based treatments . The other co‐primary outcome was prompt effective treatment defined as the proportion of febrile children who received appropriately targeted ACT treatment from a CHW within 24 h of onset of malaria symptoms, while the secondary outcome was over‐prescription defined as the proportion of children not parasite‐positive (slide negative) who received inappropriate ACT treatment from CHW .k = 0.35 in the moderate‐to‐high transmission area during the period of close support supervision, a minimum sample size of 6 patients per cluster (180 patients per arm) was required to demonstrate a 50% increase in appropriately targeted treatment with use of mRDTs from 50% to 75%, with 90% power at the 5% significance level, in a cluster‐randomised trial with 30 clusters per arm. The equivalent sample size in the low transmission area, with 30% malaria prevalence, 32 clusters per arm and inter‐cluster variation k = 0.45, to demonstrate an increase from 30% to 65% was 2 patients per cluster (128 patients per arm). Taking into account the lower proportion of cases seeking treatment within 24 h of onset of symptoms (89% and 83% in each site), sample size requirements to detect a comparable increase in prompt appropriately targeted treatment were 9 patients and 3 patients per cluster (270 and 192 per arm), in the high‐to‐moderate and low transmission areas respectively.Based on an observed prevalence of malaria of 50% and inter‐cluster variation ® Ag. P. falciparum (HRP2) Card test, www.PremierMedCorp.com; intervention arm only) performed. The mRDTs passed independent quality‐assurance batch testing by WHO‐FIND, Institut Pasteur, Cambodia. Thick blood smears were haemolysed and stained with 10% Giemsa for 10 min and examined under light microscopy with oil immersion objective lens (×100). Asexual parasites were counted against 200 white blood cells and parasite densities calculated assuming a standard white blood cell count of 8000/μl blood. A blood smear was declared negative if no parasites were detected in 100 high power fields. Reference slides included in the analysis of the primary endpoint were subjected to two independent readings by light microscopy. The second reading was performed by experienced technicians at Vector Control Division, Ministry of Health in Kampala, blinded to initial results. Any discrepant results between the first and second readings were resolved by a third technician blinded to all previous results. As the number of consultations in Bwambara (moderate‐to‐high transmission area) exceeded the sample size requirements, a subsample of blood slides was randomly selected for analysis of the primary endpoint. To reflect seasonal variation in the prevalence of malaria, two slides were randomly sampled per month from each village during the main evaluation period from January to December 2011 for double reading . Malaria was less common in Nyakashenyi, with fewer consultations, and thus, all available slides were re‐examined by expert microscopy.A finger‐prick blood sample was collected by the CHW at the time of consultation, a thick blood film for microscopy prepared, and mRDT , after the training and 6‐month period of close support supervision had ended. During 2011, a total of 13 035 children were seen by CHWs in 63 villages in Bwambara subcounty , with 5715 children seen by CHWs in the mRDT intervention arm and 7320 in the control arm  had parasite densities ≤200 parasites per μl . Despite greater delay in seeking treatment in the mRDT arm, the proportion of children who received appropriately targeted treatment within 24 h was also significantly higher in the intervention arm .Among febrile children seen by CHWs in the mRDT intervention arm, 37.5% were treated with either artemether–lumefantrine or rectal artesunate (hereafter referred to collectively as ACT) compared with almost 100% of children seen by CHWs using presumptive diagnosis . The proportion of children who received appropriately targeted treatment within 24 h was also significantly higher in the intervention arm .When compared with malaria status by microscopy, 5.8% of slide‐negative children in the mRDT intervention arm received an ACT, a significant reduction in overprescription compared with control arm, 02 Table . HoweverP < 0.001 in both sites). The effect was more marked in the low transmission area where more than 90% of the patients received appropriately targeted treatment in the mRDT arm. This could be attributable to the combination of high specificity of mRDT in a setting of low and unstable malaria transmission, where nearly 100% of mRDT negatives were negative by light microscopy, and high adherence to mRDT results by CHWs. In contrast, where CHWs followed presumptive diagnosis, more than 90% of patients subsequently found to be slide negative had received an unnecessary ACT treatment. This concurs with previous reports that overuse of ACTs is likely to be higher in areas with low malaria prevalence This study confirms that use of mRDTs by CHWs can reduce the number of ACTs used, as documented in previous studies More than 80% of patients with fever in the intervention and control arms in both transmission settings consulted CHWs for treatment within 24 h after onset of symptoms, which is much higher than has previously been observed at health facilities in Uganda The sensitivity of mRDTs in this study was lower than has been previously observed among CHWs The benefits of using mRDTs to improve targeting of ACTs and limit their overuse need to be weighed against the risks of not treating an infected child The findings presented here underscore the value of mRDT‐based diagnosis of malaria in reducing overuse of ACTs in community case management and should be of particular interest to malaria programme managers and policymakers, nationally, regionally and internationally as countries scale up iCCM


We kindly disagree with the comments of Dr. Jelliffe on the use of digoxin in critically ill patients with atrial fibrillation (AF) based on the review article “Management of Atrial Fibrillation in Critically Ill Patients” .Our daily experience in treating severely ill patients with new-onset AF shows that digoxin is helpful in reducing the heart rate (rate-control strategy) but is not useful for converting AF into sinus rhythm (rhythm-control strategy). This is in line with the current guidelines of the European Society of Cardiology  that stathe range of serum digoxin concentrations generally regarded as being therapeutic is often said to be from about 0.5 to 2.0 ng/mL (…) a few patients tolerate serum digoxin concentrations well over 3 ng/mL, and actually as high as 6.5 ng/mL, without any toxicity (…) patients with AF might require their own separate therapeutic ranges or target concentrations.”Furthermore, Dr. Jelliffe disagrees with our statement that serum levels of digoxin are recommended to support the diagnosis of intoxication but are not very helpful for titrating dose, as plasma levels do not correlate well with clinical response. He suggests measurements of plasma levels of digoxin combined with the use of the BestDose software to calculate the optimal regimen. However, in his publication Dr. Jelliffe states that “This further supports our statement that digoxin should be titrated based on clinical response and not aiming at a predefined serum concentration. The reported method for adjusting the dose of digoxin using the BestDose software is interesting but its broad implementation in daily clinical practice in ICUs is unfortunately not in sight.


Principal Treatment Centres (PTC) were established to provide age-appropriate care as well as clinical expertise for children and young people with cancer. However, little is known about the effects of specialist treatment centres on survival outcomes especially for teenagers and young adults. This population-based study aimed to describe access to PTC and the associated trends in survival for 0–24 year olds accounting for stage of disease at presentation and treatment.n = 684), lymphoma (n = 558), CNS tumours (n = 547), germ cell tumours (n = 364), soft tissue sarcomas (n = 171) and bone tumours (n = 163). Treatment was categorised into three groups: ‘all’, ‘some’ or ‘no’ treatment received at a PTC. Treatment at PTC was examined by diagnostic group and patient characteristics. Overall survival was modelled using Cox regression adjusting for case-mix including stage, treatment and other socio-demographic and clinical characteristics.Patients diagnosed from 1998–2009 aged 0–24 years were extracted from the Yorkshire Specialist Register of Cancer in Children and Young People, including information on all treating hospitals, followed-up until 31st December 2014. The six commonest cancer types were included: leukaemia  = 1.73 (95%CI 0.98–3.04)). Soft tissue sarcoma patients who had some or no treatment at PTC had better survival outcomes, which remained after adjustment for patient case-mix (adjusted HR = 0.48 (95%CI 0.23–0.99)). There were no significant differences in outcomes for other diagnostic groups . For leukaemia patients survival outcomes for low risk patients receiving no treatment at PTC were similar to high risk patients who received all treatment at PTC, implying a benefit for care at the PTC.This study demonstrates that for leukaemia patients receiving treatment at a PTC is associated with improved survival that may compensate for a poorer prognosis presentation. However, further information on risk factors is needed for all diagnostic groups in order to fully account for differences in patient case-mix.The online version of this article (doi:10.1186/s12885-017-3160-5) contains supplementary material, which is available to authorized users. Cancers in children and young people are rare, accounting for approximately 1% of all cancers diagnosed in the UK, however, cancer is one of the leading causes of death in this age group . PrincipServices for teenagers and young adults (TYA) have developed since 2005 and there are currently 25 designated TYA PTC in England . Some PTThe aims of this paper were to describe the patterns of care for the population of children and young people with cancer in Yorkshire between 1998 and 2009, therefore including periods before and after the establishment of PTCs for TYA, and assess the association with trends in survival, accounting for clinical prognostic factors which we are unique in being able to assess, notably stage and treatment using data from a specialist cancer register.Data were extracted from the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP), which is a population based database of children and young people (0–29 years) diagnosed with cancer residing in the Yorkshire and Humber region in England. The primary source of ascertainment was hospital records via the PTC with secondary sources including neuropathology reports, hospital admissions and other regional and national cancer registries . The datData on all registered neoplasms diagnosed in children (0–14 years) and TYA (15–24 years) between 1998 and 2009 were extracted. Diagnoses were categorised into histological groups according to the International Classification of Childhood Cancer 3 (ICCC-3) [For each patient we identified all hospitals where they received any treatment . Each hospital (whether in Yorkshire or outside) was classified into one of two groups: PTC or non-PTC based on designation for NHS quality reviews. Hospitals were defined as a PTC if they were designated a PTC for paediatric or TYA patients , 5. SomeIn the Yorkshire region PTCs for children and TYA are in Leeds and Sheffield; based on the study population, in Leeds a minimum of 78 children and 57 TYA were treated each year at the PTC and in Sheffield a minimum of 35 children and 18 TYA were treated at the PTC each year. The majority of our study population were treated within the Yorkshire region at these PTCs, however, patients may have been treated at other PTCs throughout the UK, and these hospitals are listed in Additional file Stage at diagnosis was available for selected diagnostic groups. White cell count was used as a proxy for stage for leukaemia; lymphoma stage was assessed using the Ann Arbor staging system; the Royal Marsden or TNM stage was used for testicular germ cell tumours and FIGO stage for ovarian germ cell tumours. We were unable to obtain sufficient stage information for bone tumours and soft tissue sarcomas. CNS tumours were categorised according to WHO grade. For each main diagnostic group further subgroups according to the ICCC-3 were extracted , treatment, relapse status, sex, year of diagnosis, ethnicity, and deprivation. Models for bone tumours also included primary site in the adjusted model. For bone tumours and soft tissue sarcomas the number of patients receiving some or no treatment at PTC were small, therefore these categories were combined. Predicted survival curves for selected groups of patients were estimated from the adjusted Cox model.Overall 2% of PTC information was missing. Levels of missing data for stage and grade varied by diagnostic group: white cell count was missing for 18% of cases, lymphoma stage missing for 46%, CNS grade missing for 55% and germ cell tumour stage for 43%. For CNS tumours with missing data, grade was assigned as low grade (WHO Grade I and II) or high grade (WHO grade III and IV) based on tumour morphology . Not allMissing PTC status, stage and grade were imputed using multiple imputation models with chained equations  and impln = 684), lymphoma (n = 558), CNS tumours (n = 547), germ cell tumours (n = 364), soft tissue sarcomas (n = 171) and bone tumours (n = 163). The total number of treating hospitals for each individual ranged from 1 to 8 (Interquartile range (1 to 2)). Overall 72% of patients received all their treatment at a PTC, 14% of patients had some treatment at a PTC (ranging from 20 to 85%) and 13% of patients received no treatment at a PTC.In total 2487 patients aged 0–24 years were included in this study: leukaemia  the risk of death increased with increasing stage or grade , with clear differences by gender reflecting different types of tumours and treatment patterns for these patients . Males wBetween 1998 and 2002 in South East England 87% of 10–14 year olds, 55% of 15–19 year olds and 32% of 20–24 year olds were treated at a either a paediatric cancer centre or teenage cancer unit [Significant differences in survival outcomes were only evident for leukaemia and soft tissue sarcomas although with differing directions of association. For all other diagnostic groups we did not find an association between place of treatment and survival.Leukaemia patients receiving all treatment at PTC had better outcomes than those who had some or no treatment at PTC. This difference was only partially explained by patient case-mix. For TYA, predicted survival outcomes were similar for high risk patients receiving all treatment at PTC and low risk patients receiving no treatment at a PTC suggesting that PTC were achieving more favourable outcomes for patients with leukaemia once risk was measured. For childhood leukaemia some studies have shown that treatment at a high volume centre or specialised centre improves survival outcomes , 20–23 wSoft tissue sarcomas patients who received some or none of their treatment at a PTC had better survival outcomes than those who had all treatment at PTC after some adjustment for patient case-mix. The main caveat associated with this finding is that we were not able to adjust for stage due to lack of information on this variable. We did however adjust for some other case-mix factors thus helping to mediate this effect.Survival from soft tissue sarcoma is one of the lowest of all diagnostic groups of cancers in children  and TYA For paediatric cancers specialised models of care are already well developed in many countries. The development of TYA specialist cancer care is beginning to appear in many European countries . In addiThe main strengths of this study were the use of high quality data including detailed information on patient case-mix, treatment and stage. We also used information on all hospitals where patients were treated. Other studies have used the first treating hospital only and this does not capture the full patient journey, for example patients who might have initially been treated in a non-specialist centre but then due to complications were referred to a specialist unit.Limitations were that we did not consider other models of care such as shared care, where patients are managed and discussed at PTC multi-disciplinary team meetings and subsequently treated at local hospitals , perhapsOur analysis provides evidence that treatment at PTC improve outcomes for leukaemia patients especially those with high risk disease. There is variation in access to PTC for children and young people with cancer, particularly for TYA. For four of the six diagnostic groups included in our study there were no differences in survival outcomes by the amount of treatment at PTC. However, to fully assess outcomes for soft tissue sarcoma patients further work is needed to ensure appropriate case-mix adjustment and replication of these findings at a national level.


RNA‐seq (n = 578), the other microarray (n = 165), quality controlled and subjected separately to network‐based analyses to identify clusters of robustly co‐expressed genes. The biological significance of these clusters was then assigned using a combination of bioinformatics analyses, literature, and expert review. After cross comparison between analyses, 20 gene signatures were defined. These included expression signatures for hair follicles, glands , keratinocytes, melanocytes, endothelia, muscle, adipocytes, immune cells, and a number of pathway systems. Collectively, we have named this resource SkinSig. SkinSig was then used in the analysis of transcriptomic datasets for 18 skin conditions, providing in‐context interpretation of these data. For instance, conventional analysis has shown there to be a decrease in keratinization and fatty metabolism with age; we more accurately define these changes to be due to loss of hair follicles and sebaceous glands. SkinSig also highlighted the over‐/under‐representation of various cell types in skin diseases, reflecting an influx in immune cells in inflammatory disorders and a relative reduction in other cell types. Overall, our analyses demonstrate the value of this new resource in defining the functional profile of skin cell types and appendages, and in improving the interpretation of disease data. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.Numerous studies have explored the altered transcriptional landscape associated with skin diseases to understand the nature of these disorders. However, data interpretation represents a significant challenge due to a lack of good maker sets for many of the specialized cell types that make up this tissue, whose composition may fundamentally alter during disease. Here we have sought to derive expression signatures that define the various cell types and structures that make up human skin, and demonstrate how they can be used to aid the interpretation of transcriptomic data derived from this organ. Two large normal skin transcriptomic datasets were identified, one  Disorders of the skin are ranked as the fourth leading cause of non‐fatal disease burden et alet alet alIn an effort to address this problem, Swindell We therefore set out to identify sets of genes diagnostic of the many cell types present in human skin. Our approach is based on the fact that the expression level of a gene expressed specifically in a particular cell type or skin appendage will vary according to its abundance in a given sample. When measured across multiple samples, any other genes expressed in a similar manner will be observed to be co‐expressed. When correlation networks are constructed from transcriptomic data SkinSig, for skin appendages, cells, and processes present in human skin. This resource aids the interpretation of transcriptomic data derived from human skin, allowing the cellular composition of samples to be explored. Furthermore, these signatures enable the pathological and physiological changes associated with skin conditions, disease subtypes or therapeutic interventions to be characterized.Here, we describe the derivation of 20 highly conserved gene expression signatures, collectively named http://www.gtexportal.org) The RNA‐seq dataset  was obtained from the GTEx project  performed on the Affymetrix U133 plus 2.0 array and downloaded from the Gene Expression Omnibus (GEO) r) set at r ≥ 0.73. A similar process was repeated for the microarray dataset at probe level with a correlation threshold of r ≥ 0.66. The Markov clustering (MCL) algorithm Of the 56 318 transcripts (genes defined in GENCODE v19) in the original RNA‐seq dataset, 24 128 transcripts were expressed in normal skin (>1 RPKM in at least one sample). The filtered dataset of skin‐expressed transcripts was loaded into Miru, with a gene–gene Pearson correlation threshold , pathway inspection , and protein localization . In addition, co‐expression signatures from previous studies were manually compared with clusters derived from skin, allowing the naming of some of the signatures. Clusters without GO enrichment or without similarity to previously reported co‐expression signatures were further investigated by checking individual genes against the literature and the phenotypes reported for knockout mice.In order to identify the functional relevance of transcript clusters, we used a combination of bioinformatics tools, literature review, as well as similarity to previously defined co‐expression clusters In order to compare across analyses, the RNA‐seq and the microarray data were mapped to HGNC gene symbols , which were used as common identifiers. Where more than one microarray probe targeted a given symbol, the probe set with the highest median absolute deviation for signal intensity was used. Both datasets were further filtered to include only genes common to both platforms and with an expression greater than 1 RPKM in at least one sample in the RNA‐seq dataset. A total of 15 736 transcripts met these criteria.r ≥ 0.66 for the microarray dataset and r ≥ 0.73 for the RNA‐seq dataset. Network‐derived clusters from each study were individually annotated with the assistance of signature tracks imported from the network analysis of the full transcript/probe set analyses. Genes with the same annotation for both studies were used to construct SkinSig, together with three study‐specific annotations .Correlation networks were created from the common‐symbol filtered datasets at SkinSig in analysing gene expression data were demonstrated using existing microarray or RNA‐seq datasets from a variety of skin conditions. The combined dataset originally organized and normalized by Inkeles et alet alThe utilities of 2 prior to averaging). The log fold changes between these values from the pairing test and control groups were used to plot a heatmap, thereby allowing comparison across the different skin conditions. Positive fold change reflects overrepresentation of the signature in the test group, i.e. a particular skin condition. Details on each test–control pairing are described in the supplementary material, Table S1. Rotation gene set test (ROAST)  For each dataset, the geometric means were used to average across multiple probes for the same HGNC symbol. The expression level of each signature was calculated as an average of all genes within a signature . Co‐expression network analyses were carried out on the keratinocyte differentiation signature for each study, with only normal skin, only psoriatic skin or all samples. By using the MCL algorithm  on the co‐expression network for all samples from GSE13355, two subgroups of the keratinocyte differentiation signature were identified. ROAST was used to determine whether the keratinocyte differentiation subgroups were significantly differentially expressed. An FDR ≤ 0.01 was considered to be significant.The study only involved publicly available de‐identified data; ethical approval was not required in the country/region in which the study was carried out .n = 165). In addition, a collection of RNA‐seq data from biopsies of post‐mortem normal skin from lower leg or suprapubic regions (n = 578) was obtained from the Genotype‐Tissue Expression (GTEx) project Two datasets representing large collections of normal skin biopsies were selected (GSE13355 and GSE30999) We interrogated these datasets using co‐expression network analysis Following QC and batch correction, both the RNA‐seq and the microarray datasets showed little overall variation between samples, suggesting little variation attributable to technical or biological factors . Although the microarray and RNA‐seq datasets differed in a number of respects , the median expression levels for the majority of genes expressed across the two studies were consistent . However, genes with little expression  demonstrated a non‐linear relationship between the two platforms. Saturation of microarray probes and a non‐linear signal response for highly expressed genes is a known limitation of microarray analyses, and here, the signal intensity plateaued at a signal intensity value of approximately 16 000 . A small number of genes are observed to be very highly expressed in RNA‐seq data relative to others .r value used to construct each network was determined empirically. At an r value of 0.73, the RNA‐seq dataset yielded a co‐expression network which was composed of 10 336 nodes (genes), connected by 114 904 edges, and contained 927 clusters  which are used in the generation of co‐expression networks. Use of the MCL algorithm ABCC11 and ACSM1 , neurones (Cluster 43), and glial cells (Cluster 83) . Three clusters of genes were unexpected and inexplicable in skin samples; Cluster 1  contained many genes involved in spermatogenesis, Cluster 29 (pancreas) contained genes encoding pancreatic enzymes , whereas Cluster 97 (stomach mucosa) contained genes encoding gastrokine, gastric lipase, and pepsinogens . However, many of the remaining clusters observed in the RNA‐seq dataset were found to show significant overlap with gene clusters present in the microarray dataset (see below). Exceptions included a cluster of genes exclusively expressed in the suprapubic samples which contained several apocrine gland markers, e.g. 2, neuronWe identified 17 signatures with overlapping gene membership in both the RNA‐seq and the microarray datasets Figure A. These SkinSig.In addition to these 17 signatures, three additional signatures that were only observed in one of the two datasets were also included: circadian clock (microarray only), apocrine gland, and skeletal muscle (RNA‐seq only) , and Y‐chromosome. The justification and relevant literature supporting the functional annotation of these signatures are discussed in the supplementary material, Supplementary discussion. Our analyses obtained several novel marker gene signatures defined here for the first time. These include signatures for hair follicles, sebaceous glands, eccrine sweat glands, apocrine glands, and melanocytes. The localization of proteins encoded by some of these genes is independently confirmed in immunohistochemistry images derived from the Human Protein Atlas SkinSig, we gathered transcriptomic data from studies of 18 different skin conditions . These included the combined dataset reported by Inkeles et alSkinSig gene signature was calculated and compared with control samples . Each skin condition had a specific profile of altered gene expression of the SkinSig signatures. Hierarchical clustering of the signatures  based on these analyses revealed three main groupings of skin conditions  We also analysed an additional dataset comprising normal and psoriatic skin  SkinSig revealed significant changes (FDR ≤ 0.01) to gene signatures associated with hair follicles and sebaceous gland in ageing skin , and an expanded list of annotated gene clusters derived from these studies, including the analysis of the data from the MuTHER study Here, co‐expression network analysis has been used to interrogate transcriptomic data derived from normal human skin. Using this approach, we derived 20 co‐expression signatures that characterize the function‐specific profile of many cell types and appendages present in the skin. The most conserved of these co‐expressed genes across the two primary datasets used for this study have been named SkinSig was demonstrated by using the signatures to describe the transcriptional changes associated with 18 skin conditions and ageing. SkinSig broadly separated the skin conditions into three groups  signatures , together with down‐regulated keratinocyte differentiation and melanocyte signatures  Figure . The oveSkinSig signatures consistent with the known pathology of those conditions, such as the increased expression of the T‐cell signature in allergic contact dermatitis Group 3 generally showed less transcriptional perturbation relative to normal skin. UV‐challenged skin and vitiligo were most similar to normal skin, with the former showing an increase and the latter a decrease in the expression of the melanocyte signature Figure . UV expoThis study highlights the need to consider the sampling protocols used when interpreting transcriptomic data Figure . Macro‐dLCE1B, LCE2B, FLG2, and LOR, are known to be associated with the terminally differentiated keratinocytes (cornecytes) that make up the stratum corneum. Furthermore, LCE3B and LCE3C deletions have been identified as risk factors for psoriasis in multiple ethnic groups SkinSig allows the recognition of a disrupted system by defining the dynamics between genes in a physiologically normal state.Intriguingly, the keratinocyte differentiation signature did not appear to be up‐regulated in any of the skin conditions, including psoriasis. Upon closer examination of the expression of this signature in three independent psoriasis datasets, two subgroups of the keratinocyte differentiation signature genes were identified, and the ratio between the expression levels of these two subgroups was altered in psoriatic skin Figure C. Higheret alSkinSig, these changes may more accurately be described as a reduction in the number or functional activity of sebaceous glands and hair follicles with age  Sample–sample correlation plots of data used in these studies, using the maximum Pearson correlation coefficient threshold that still retained all samples for the RNA‐seq (r ≥ 0.93) and (B) the microarray (r ≥ 0.97) datasets. There is minimal sample separation due to the main sample attribute, i.e. study (microarray) or site of sampling (RNA‐seq). (C) Log‐scale plot of median expression values, showing that expression levels for the majority of genes are positively correlated in the two datasets. (D) Of the top ten genes with the highest expression for the microarray (red) and RNA‐seq (blue) datasets, there were only two genes in common (RPS18 and KRT14). A higher dynamic range was also observed in the RNA‐seq dataset.Click here for additional data file.Figure S2 Pearson correlation thresholds in randomized and original data. (A) When expression values for each gene were randomized across the samples of the RNA‐seq dataset, only 95 pairing (edges) were observed at a threshold used in this analysis (r ≥ 0.73), whilst the untransformed data yielded 87 121 edges. Taking into account that a total of 123 802 980 calculations were made for every possible combination of gene–gene Pearson correlation analysis, the frequency of a pair of genes reaching the r threshold is 7.7 × 10−7 (blue), compared with a frequency of 7.0 × 10−4 for non‐random correlations observed in the actual dataset (red). This supports the notion that the vast majority of relationships used to build the network analysed here are non‐random. (B) Similar analysis was done to the microarray dataset. The randomized version of the dataset shows a frequency of 1 × 10−8 for a random correlation (blue) to occur at r ≥ 0.66, compared with a frequency of 6.7 × 10−4 observed in the actual dataset (red).Click here for additional data file.Table S1 Details on the datasets used in this study The subject details, experimental design, data source, and comparisons for all datasets included in this study are listed in Table S1. This includes the three primary data datasets, 22 validation datasets for skin conditions, three psoriasis datasets for integrating the keratinocyte differentiation signature, and one ageing dataset.Click here for additional data file.SkinSig and co‐expression signatures derived from the analysis of different datasets derived from normal human skinTable S2  The supplementary file includes the co‐expression signatures for all network analysis, including the individual full original datasets, gene‐symbol restricted datasets, and SkinSig.Click here for additional data file.Table S3 Gene ontology enrichment and example genes for each signature In addition to highlighting a number of key genes and significant gene ontology (GO) enrichment result for each SkinSig, the supplementary file also includes the full set results from gene ontology enrichment.Click here for additional data file.SkinSig and genes found to be significantly altered during ageingTable S4 Comparison between  Genes reported to change in their expression during ageing are compared with SkinSig, showing a high percentage of the sebaceous gland and hair signatures being reported to alter with ageing by Glass et al [19].Click here for additional data file.