Nasopharyngeal carcinoma (NPC) is endemic in Indonesia and 20% of the patients are diagnosed before the age of 31. This study evaluates presentation and treatment outcome of young patients in Jakarta, in a tertiary referral centre.Forty-nine patients under the age of 31, diagnosed with NPC between July 2004 and January 2007, were evaluated. Baseline data included histological type, stage of disease and presenting symptoms. We intended to follow all patients after diagnosis to reveal treatment outcome and overall survival (OS).All but two patients had advanced stage disease (94%), 7 (14%) had distant metastasis. The median interval between start of complaints and diagnosis was 9 months. Forty-two patients were planned for curative intent treatment. Eleven patients (26%) never started treatment, 2 patients did not complete treatment and 3 patients did not return after finishing treatment. Four patients died before radiation could start. Three patients died within 4 months after treatment. Nine patients (21%) had a complete response. Due to the high number of patients who were lost to follow-up (LFU), OS was analyzed as follows: a best-case (patients censored at last contact) and a worst-case scenario (assuming that patients who did not finish treatment or had disease at last contact would have died). The 2-year OS for patients without distant metastases was 39–71%.Treatment outcome for young patients with NPC in this institute was poor. Improvement can be achieved when NPC is diagnosed at an earlier stage and when there is better treatment compliance. The incidence of nasopharyngeal carcinoma (NPC) in Indonesia is estimated to be 6∶100.000, meaning that every month at least 1000 patients are diagnosed. Probably related to better diagnostics and improved awareness this number increases every year NPC in young patients differs in certain aspects from adults. The percentage of non-keratinizing undifferentiated carcinoma is higher, and the association with Epstein-Barr virus (EBV) is stronger Treatment for young patients generally follows the guidelines established for adults; radiotherapy on the nasopharynx and cervical nodal levels, usually combined with chemotherapy All these results are derived from top-end hospitals and some are in clinical trial settings. Here we present a prospective observational study on routine treatment results of young patients with NPC at a top end hospital in Jakarta. We will describe the tumor characteristics and complaints at presentation, the given treatment and the treatment outcome.This was a prospective cohort study. All patients diagnosed with NPC between July 2004 and January 2007 at the Rumah Sakit Cipto Mangunkusumo (RSCM), a university hospital in Jakarta, were eligible for inclusion. Patients were included if they were below the age of 31 at diagnosis and had histological proven NPC. In this period 228 patients were diagnosed with NPC, and 49 patients met the inclusion criteria of age and histological confirmed NPC. Ethical approval was obtained at the Ethical committee of the Faculty of Medicine of the university of Indonesia. All patients or their parents/ legal guardian signed informed consent. To get more insight in the specific problems of patients at young age, the patients were divided into two groups, i.e. ≤15 years and >15–30 years.Baseline information consisted of patient demographics, including the type of insurance. Jakarta has three types of insurances; jamkesmas (poor people), askes (civil servants) and patients who pay health care out of the pocket or have private insurance (self-finance) Information on symptoms was gathered from the clinical medical record. Symptoms were scored for presence at diagnosis and duration till diagnosis. The histological diagnosis was made according to the World Health Organization (WHO) classification, WHO type 1; keratinizing squamous cell carcinoma, WHO type 2; non-keratinizing squamous cell carcinoma, WHO type 3; undifferentiated carcinoma. The extent of disease was determined by clinical examination using rigid or flexible nasopharyngoscopy, Computed Tomography (CT)-scan of the head and neck region, chest radiography, ultrasonography of the abdomen and a bone scan. Tumor stage was classified according to the 2002 criteria of the 6th American Joint Committee on Cancer (AJCC).Due to the waiting time to radiation, different schedules were used. Three different radiation schedules were used; conventional fractionated schedule ; hyper fractionated schedule ; accelerated hyper fractionated schedule . All schedules could be completed in 6–7 weeks. Neo-adjuvant chemotherapy consisted of intravenous cisplatin 100 mg/m2/day on day 1, and 5-fluoro-uracil 1000 mg/m2/day on day 1–5, every 3 weeks for 3–4 courses. Concurrent chemotherapy consisted of intravenous cisplatin 40 mg/m2 weekly during radiotherapy.Patients with distant metastasis received palliative chemotherapy; cisplatin 100 mg/m2/day on day 1 and, 5-fluoro-urasil 1000 mg/m2/day on day 1–4. The number of courses depended on the clinical condition. Palliative radiotherapy was given on bone metastasis.Patients were scheduled for routine follow-up at the outpatient clinic. Treatment response measurements were planned 8 to 12 weeks after treatment, by physical examination, nasopharyngoscopy and CT-scan. The follow-up schedule proceeds with 3 monthly visits during the first 2 years after radiotherapy.To test for association between age and tumor stage at diagnosis, linear-by-linear test was used. For symptoms at diagnosis two scales were constructed: the number of complaints at diagnosis and the maximum duration to diagnosis. For patient's missing data on symptom duration, the median duration was imputed. Associations between these two scales and both age (as a continuous variable) and AJCC stage were tested using linear-by-linear tests.Association between age and diagnosis-to-treatment interval (DTI) and overall-radiotherapy-treatment time (OTT) was assessed by Spearman correlation test. The Statistical Package for the Social Sciences, version 20 was used for analysis. P-values less than 0.05 were considered as significant.Kaplan-Meier analyzed overall survival. Survival time was defined as the time between the date of diagnosis till the date of death. Stratification was done by M stage, and M0 was further stratified by age (0–15 and 16–30). For comparison between the 0–15 and the 16–30 age group a log rank test was used.Forty-nine patients were included. The median age was 21 and ranged between 3–30 years. WHO type 3 was the histological type in 46 patients (94%) and 3 patients (6%) had WHO type 1. The mean follow-up period for the patients without distant metastasis at diagnosis was 18 months and for patients with distant metastasis 7 months.T-stage was dominated by advanced stage (66%). Lymph node metastasis was seen in 96% of the patients . Ninety-Information on presenting symptoms at diagnosis was available for 41 patients . The medNo associations (linear-by-linear) were detected between either age and the number of complains at diagnosis (p = 0.41) or the duration to diagnosis (p = 0.79). Also no associations were found between the stage of disease at diagnosis and the number of complaints (p = 0.25) and interval to diagnosis (p = 0.29).Forty-two patients could be planned for treatment with curative intent. For 22 patients data was available on given radiotherapy treatment . The medFor 18 patients data on the overall radiotherapy treatment time (OTT) was available. The median OTT was 55 days (range 38–160), no association with age was found (spearman correlation p = 0.41). Since almost all patients had jamkesmas insurance, no association between the insurance type and the DTI or OTT could be found.Directly after diagnosis 11 patients (26%) did not return to the hospital. Two patients stopped therapy during treatment, and directly after therapy 3 patients never returned to the hospital. Despite several attempts to contact these 16 patients, no information on their health status could be retrieved. Four patients died before radiation treatment could start. Accordingly, for 22 patients the effect of treatment could be studied. Three patients died within 4 months after radiotherapy, before response was assessed. For three patients survival data was available, but no data on therapy response; one died 19 months after neo-adjuvant chemotherapy (unknown if he finished radiation treatment), one was alive 30 months after radiation and clinical suspect for distant metastases, and one died 65 months after radiation (reason unknown).Nine patients underwent examination 2–3 months after treatment, one patient had the examination 1 month after treatment and six patients had examination later than 3 months after therapy. Complete response was seen in 9 of these 16 patients, partial response in 5 patients and progressive disease in 2 patients.The number of patients who were lost to follow-up (LFU) in this study was high. Despite multiple attempts to contact them or their family, it was not possible to minimize the missing data. The assumption that the risk to death was equally distributed between patients who were LFU and patients who were still in the study is not likely. This is based on the fact that some patients never started treatment, stopped during treatment or had disease at the last date of follow-up. Therefore we made two Kaplan-Meier curves, representing a best-case scenario and a worst-case scenario. The best-case scenario is a regular Kaplan-Meier curve, wherein all patients are censored on the last date of follow-up. For the worst-case scenario; for patients without distant metastasis, all patients who did not return to the hospital before starting treatment (n = 11), before finishing treatment (n = 2), or who had disease at last moment of contact (n = 6) were assumed to be death at the last date of contact; for patients with distant metastasis at diagnosis, the last date of contact was set as the date of death. A realistic overall survival curve will be positioned between these two Kaplan-Meier curves.The 2-year overall survival for patients without distant metastasis at diagnosis was 39–71% . The 2-year survival for patients with distant metastasis at diagnosis was 0% . OverallOverall survival was tested on association with stage of disease, symptoms at diagnosis, waiting time for radiotherapy and treatment duration. No significant results were found. It was not possible to test for association with insurance, since the group of patients with other insurance than jamkesmas was too small.Cancer is the leading cause of death worldwide Cancer care for young patients with NPC in Jakarta is poor compared to international literature. In literature, 1–4% of the young patients with NPC have distant metastasis at initial diagnosis Advanced stage of disease at diagnosis was seen in 94% of the patients. Since stage of disease is strongly associated with prognosis, this partly accounts for the poor survival. Advanced stage at diagnosis is related to a long interval to diagnosis Neck masses, a late stage symptom, were present in 93 per cent of our patients at diagnosis. In almost all patients this complaint existed with the longest interval to diagnosis. One should assume that when a neck mass is present, a patient (or parent) should make effort to consult a doctor. Instead, a time interval of 9.5 months was found before definitive diagnosis. It seems that patients (and probably doctors) are not aware of the probability for NPC involvement in young patients with an unexplained neck mass.Patient's delay to diagnosis can also be caused by the long distance to health care facilities or limited financial resources of patients, 84% had poor men's insurance. Besides, we know by experience that many patients first seek medical help in the alternative circuit. Even when NPC is diagnosed some patients prefer alternative therapy above conventional. We cannot confirm this by our study results, but eleven patients did not return to the hospital after diagnosis. Unfortunately, we could not retrieve the reason for not returning. More public awareness about the symptoms of NPC and need for early treatment with (chemo-) radiotherapy can contribute to an earlier consultation of the doctor and better compliance to the advised therapy. Previous studies have already shown the effectiveness of public awareness campaigns in breast and cervical cancer As mentioned before, the doctor can also cause the delay to diagnosis; when doctors do not recognize the symptoms as related to cancer or when they are not aware of the high probability of NPC. Earlier research revealed that the knowledge of general practitioners (GP's) on NPC in Indonesia was insufficient Another result of this study was the insufficiency of the treatment itself. The median interval between diagnosis and radiotherapy was almost 4 months. This is partly caused by the insurance system. Almost all patients had jamkesmas insurance. Hereby, approval is needed for every investigation and treatment, which takes valuable time. Another key reason for the long interval to treatment is shortage in capacity of radiotherapy facilities. In 2008, 35 radiotherapy devices were available for a population of 229 million. A substantial number of these devices are out of order on a regular base, resulting in 0.13 accelerators per million inhabitants The long waiting time, the neo-adjuvant chemotherapy to overcome the waiting time and the treatment of radiation (with or without concurrent chemotherapy) has impact on patient's physical status. In this study three patients died before treatment could start, one patient died during neo-adjuvant treatment and another four patients died directly after treatment, accordingly 19% died soon after diagnosis. This percentage might be an underestimation, since directly after diagnosis 11 patients were lost to follow-up. These patients did not get treatment, so it is assumable that some of them also would have died. The results are comparable to a recent study of adults with NPC, conducted in Yogyakarta, here 13% of the patients died before radiotherapy started and 29% died before treatment response could be assessed The overall treatment time of radiotherapy was 55 days. Optimally, a total dose of 66 to 70 Gray should be given in 33–35 fractions in a maximum of 47 days. For each day by which radiotherapy treatment is extended, effective dose is lost, and the success rate declines rapidly Another problem that we encountered was the lack of data management and poor follow-up. This made it impossible to compare the different treatment protocols and made statistical analysis difficult. In general the lack of proper data management causes a lack of essential feedback for doctors, which results in the absence of a learning curve and current insight in problems in cancer care in general. Besides, poor follow-up results in late recognition of recurrent disease, which immediately affects the patient's health and chances of survival. A digital data management system may result in better insights in clinical performance and stimulate the treatment learning curve This is the first study presenting the treatment results of young patients with NPC in Indonesia, where 20% of the patients are diagnosed before the age of 31. Comparable, poor treatment outcome has been found in an independent study among adults with NPC in Yogyakarta, and it is assumable that other low and middle-income countries are coping with similar problems in handling NPC patients Establishing more radiotherapy facilities would be the best step to solve a big part of the problems. However, even when financial resources are not the limiting factor, it will take a decade to built new bunkers and educate doctors and nurses to accomplish this. In the meanwhile the focus should be to treat people who can have treatment in a proper way. Earlier diagnosis, better treatment compliance and improved follow-up are the key points to accomplish this. More public, medical and patient awareness for these key points might be one of the answers.


Indeed, one of the earliest events following the perception of environmental changes  is intracellular variation of free calcium concentrations. These calcium variations differ in their spatio-temporal characteristics  with the nature and strength of the stimulus and, for this reason, they are considered as signatures encrypting information from the initial stimulus. This information is believed to drive a specific response by decoding via calcium-binding proteins. Based on recent examples, we illustrate how individual calcium sensors from the calcium-dependent protein kinase and calmodulin-like protein families can integrate inputs from various environmental changes. Focusing on members of these two families, shown to be involved in plant responses to both abiotic and biotic stimuli, we discuss their role as key hubs and we put forward hypotheses explaining how they can drive the signaling pathways toward the appropriate plant responses.The Ca Such Ca2+ transients, each linked to a particular stimulus, differ in their spatio-temporal patterns, being therefore considered as signatures  is mandatory. Calcium sensor proteins estimated to number over 250 in Arabidopsis  and salicylic acid (SA). Salt-responsive expression of AtCML9 is dependent on ABA production, while its expression in response to a virulent strain of Pseudomonas syringae is dependent on SA synthesis, suggesting that this CML is involved in stress hormone-mediated responses. In this respect, Atcml9 null mutants exhibit a hypersensitive response to ABA during early growth stages that could be correlated with the enhanced tolerance to drought and salt stress in adult plants. Atcml9 mutants also show alterations in the expression of several stress and ABA-responsive genes as well as defense-related genes. These data indicate that AtCML9 contributes to both abiotic and biotic stress responses in conjunction with hormone signaling, and suggest a role for AtCML9 in the regulation of stress/defense-related genes. AtCML9 was reported to interact with diverse transcription factors including WRKY53 and TGA3 two factors that participate in plant defense responses -responsive genes and an increased accumulation of aliphatic glucosinolates . CAD comprises an inhibitory junction domain  fused to a CaM-like domain  produced by Fusarium monoliforme, is able to quickly increase the level of free LCBs in the cell thus mimicking a LCB treatment  that likely activates the transcription of the defensin gene PDF1.2, known to be induced as a resistance mechanism , we identified AtCPK3 as a crucial effector in this process . Long chreatment . In addiycotoxin . In paraechanism . Besideschannels . In the response  whereas response .AtCPK1, which when over-expressed confers a SA-mediated resistance phenotype in Arabidopsis toward both necrotrophic fungi  and biotrophic bacteria (Pseudomonas syringae), while its suppression (null mutants) results in an enhanced susceptibility to these pathogens  were found able to activate the expression of the Ca2+-dependent flg22-responsive reporter gene NHL10-LUC (NDR1/Hin1-Like10-LUCiferase), in the same manner as flg22 (RD29A). The activity of LUC was synergistically increased upon co-expression of AtCPK4 with the ABA-responsive bZIP transcription factor ABF2, whereas AtCPK11 which shares more than 95% identity with AtCPK4 was unable to enhance the LUC response through ABF2 . Taking AtCPK3, as a model, we can speculate on the way in which a single Ca2+ sensor can wire the flow of signaling information toward the right adaptive response upon challenging the plant with different stimuli. Beyond the importance of the specific Ca2+ signature generated in response to a given stimulus, it is clear that specificity of the routing will be achieved only if the calcium sensor, its downstream targets and putative partner proteins are co-localized and expressed at the same time. In guard cells, AtCPK3 can be activated by calcium ions flowing through non-selective calcium channels in response to environmental cues  . It can then be hypothesized that these different classes of activated channels can engage AtCPK3 in different signaling modules that are specifically associated to each type of channel. Such an association will favor the interaction of AtCPK3 with its downstream substrates specifically dedicated to one output (defense or cell death). Similar assemblies of signaling proteins defined as signaling complexes or scaffolds have been shown for a few signaling proteins in plants. Indeed AtCPK21 was shown to interact in an ABA-dependent manner with the slow anion channel 1 (SLAC1) homolog 3 (SLAH3) in microdomains of the guard cell plasma membrane. The activity of AtCPK21, which is crucial for SLAH3 activation, seems to be regulated by a protein scaffold involving protein phosphatase 2C (PP2C) ABI1 and an ABA receptor of the RCAR1/PYR/PYL family   and can eceptors  that hav+ influx . These Crbivores . Howeverith LCBs . A diffeL family . This scL family  where clreported .2+ sensor acts at the crossroads of various signaling pathways to help plants to cope with various environmental challenges.The operating mode of these signaling hubs remains a challenging question that needs further investigation. Emerging imaging techniques at nanoscale resolution  and new Figure 1 and in critically revising the manuscript with PT, J-PG, and VC.CM and BR contributed to the writing of the manuscript. DA and CM participated in drawing The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In clinical research prediction models are used to accurately predict the outcome of the patients based on some of their characteristics. For high-dimensional prediction models (the number of variables greatly exceeds the number of samples) the choice of an appropriate classifier is crucial as it was observed that no single classification algorithm performs optimally for all types of data. Boosting was proposed as a method that combines the classification results obtained using base classifiers, where the sample weights are sequentially adjusted based on the performance in previous iterations. Generally boosting outperforms any individual classifier, but studies with high-dimensional data showed that the most standard boosting algorithm, AdaBoost.M1, cannot significantly improve the performance of its base classier. Recently other boosting algorithms were proposed ; they were shown to perform better than AdaBoost.M1 but their performance was not evaluated for high-dimensional data.In this paper we use simulation studies and real gene-expression data sets to evaluate the performance of boosting algorithms when data are high-dimensional. Our results confirm that AdaBoost.M1 can perform poorly in this setting, often failing to improve the performance of its base classifier. We provide the explanation for this and propose a modification, AdaBoost.M1.ICV, which uses cross-validated estimates of the prediction errors and outperforms the original algorithm when data are high-dimensional. The use of AdaBoost.M1.ICV is advisable when the base classifier overfits the training data: the number of variables is large, the number of samples is small, and/or the difference between the classes is large. To a lesser extent also Gradient boosting suffers from similar problems. Contrary to the findings for the low-dimensional data, shrinkage does not improve the performance of Gradient boosting when data are high-dimensional, however it is beneficial for Stochastic Gradient boosting, which outperformed the other boosting algorithms in our analyses. LogitBoost suffers from overfitting and generally performs poorly.The results show that boosting can substantially improve the performance of its base classifier also when data are high-dimensional. However, not all boosting algorithms perform equally well. LogitBoost, AdaBoost.M1 and Gradient boosting seem less useful for this type of data. Overall, Stochastic Gradient boosting with shrinkage and AdaBoost.M1.ICV seem to be the preferable choices for high-dimensional class-prediction.The online version of this article (doi:10.1186/s12859-015-0723-9) contains supplementary material, which is available to authorized users. The goal of class prediction (classification) is to develop a rule (classifier) based on the values of the variables measured on a group of samples with known class membership (training set). This rule can be used to predict the class membership for samples with unknown class membership but for which the values of the variables used by the rule are known . In clinThe new technological advances allow the biomedical researchers to measure the expression of ten thousands of genes, or over one million single nucleotide polymorphisms (SNPs), in a single assay. These technologies are increasingly often used with the aim to develop personalized treatments or individualized drug selection , 8. For Ensemble classifiers are combinations of many different classifiers whose outputs are combined into a single vote by majority voting or weighted majority voting. They can be useful because combining a set of classifiers can result in more accurate predictions . Recentlin silico vaccine discovery pipeline for eukaryotic pathogens [Boosting was applied to high-dimensional data, using mostly gene expression data, by Ben-Dor et al.  and Dudoathogens  or MeSH athogens .Dettling and Bühlmann  proposedAdaBoost.M1 was shown to be equivalent to forward stagewise additive modeling using the exponential loss function . Based oIn this paper we investigate how boosting algorithms are affected by high-dimensionality, limiting our interest to two-class prediction problems and class-balanced data, i.e. a situation where the number of samples from both classes is approximately the same. We first show that when it is easy to overfit the training data with the base classifier, AdaBoost.M1 and GrBoost algorithms perform exactly as their base classifiers, which can explain the poor performance of AdaBoost.M1 presented in , 23, 29.We evaluated the performance of AdaBoost.M1, gradient boosting (GrBoost), LogitBoost and of AdaBoost.M1.ICV, an algorithm that we propose in this paper, on high-dimensional data. The base classifiers were classification trees where maximum depth was set to 5 (CART(5)) or 1 ). The acronyms used to indicate the specific boosting algorithms are reported in Table Briefly, in AdaBoost.M1 at each boosting iteration the weights applied to the training observations depend on the training (re-substitution) error ; samplesη of the training data is sampled without replacement and the tree is grown using only that subsample: this approach is known as Stochastic Gradient boosting (St-GrBoost). In our simulations η was set to 0.5 [ν<1, ν=1 is no shrinkage, in our analyses the shrinkage factor was set to ν=0.001, 0.01, 0.1 and 1). In the low-dimensional setting it was suggested that ν should be set to a very small number and than one should choose the number of iterations (M) by early stopping [ν (more shrinkage) lead to larger number of boosting iterations for the same training error, so there is a trade-off between them, which can be an issue in the high-dimensional setting because of larger computational burden.Exponential loss was used for gradient boosting and we investigated the performance of two regularization strategies: shrinkage and sub-sampling. With sub-sampling at each boosting iteration a fraction t to 0.5 . With shstopping . HoweverThe simulations were performed to evaluate the performance of different boosting algorithms on high-dimensional data.ρ=0.8), while those from different blocks were independent . ThThe performance of the classifiers in all simulation settings was evaluated on independent test sets containing 500 samples from each class and the results were reported as averages from 100 iterations. Next, we give the exact simulation settings for each subsection appearing in the “p=1000, the number of training set samples was ntrain=50 and the difference between the classes was μ2=0.7. We considered up to M=1000 boosting iterations.The number of variables was set to ρ=0), the number of variables ranged from 10 to 10000 and the size of the training set ranged from 50 to 1000. There was no difference between the classes, μ2=0. We evaluated the resubstitution error rate of CART(5) and CART(1).We performed also a set of simulations used to illustrate the effect of high-dimensionality on the overfitting of CART(5) and CART(1). We simulated independent standard normal variables . These results are reported as Additional information.We performed also a set of simulations where we adjusted the differences between the classes so as to achieve approximately the same test set errors using CART(5) for all values of ntrain= 50, 100, 200, 500 and 1000. For each of these settings the number of variables was set to p=1000 and 2500, while for each ntrain and p combination the difference between the classes was set to μ2= 0.5 or 1.The number of samples in the training set was set to To reduce the computing time, the number of boosting iterations in each ensemble was set to 10, 100, 200 or 300; additionally we considered also 500 iterations as well as the optimal number of boosting iterations based on the out-of-bag estimate for Stochastic Gradient boosting.T,diag), while the 20 differentially expressed variables were simulated from MVNT,diag) for 50 class 1 samples and MVNT,diag) for 50 class 2 samples. Up to M= 500 boosting iterations were considered.In this setting the mean for all variables for both classes was equal, while the variability of some variables was different in the two classes. This enabled us to simulate a data structure were samples from one class were nested within the samples from the other class. We simulated 980 or 9980 variables from MVNp=1000, the number of training set samples was ntrain=50 and the difference between the classes was μ2=0.7.The number of variables was set to M=1000 boosting iterations, while the left-out fold was used to estimate the classification error for each of the M=1,…,1000 iterations. The 5-fold cross-validated number of iterations was defined as the number of iterations where the cross-validated error rate  was the smallest. In case of ties we selected the smallest number of iterations. Similarly, we determined the leave-one-out cross-validated number of iterations, where exactly one sample was included in each of the left out folds. Additionally, we report also the optimal number of iterations, which is defined as the number of iterations where the minimum test set error rate  is achieved over the 1000 boosting iterations.We used 5-fold cross-validation to determine the cross-validated number of iterations, i.e. the training set was split into 5 folds, 4 folds were used to train the classifiers using nmin,nmax) samples from the majority class were selected and included in the training set. This strategy was shown to perform well with high-dimensional data [We reanalyzed the breast cancer microarray gene expression data of Sotirou et al. , Wang etnal data .Table 21, i.e., PA evaluated using only samples from class 1), (iii) predictive accuracy of class 2 (PA2), (iv) Area Under the Receiver-Characteristic-Operating Curve (AUC) [Five measures of classifier’s performance were considered: (i) overall predictive accuracy , (ii) predictive accuracy of class 1 (PAve (AUC)  and (v) We used Wilcoxon signed-rank test to test if there was a statistically significant difference between g-means obtained with AdaBoost.M1.ICV(5) and the other classifiers across various different simulation settings . For each classifier, the number of boosting iterations that achieved the best classification performance in terms of g-means was considered. Because of a large number of comparisons the p-values were adjusted with the Holm’s method to control the type I error . An adjuLogitBoost in package caTools was used to perform LogitBoost, functions gbm.fit and gbm.perf from gbm package to perform GrBoost and St-GrBoost, while the other ensemble classifiers were programmed in R by the authors; the R-code is available upon request.All the analyses were performed with R language for statistical computing, version 3.0.0 . The funIn this section we present the results based on our simulation studies and the reanalysis of microarray gene expression data. The complete simulation settings appear in the “Here we report a selected series of simulation results used to illustrate the performance of AdaBoost.M1 when the data are high-dimensional Table .Table 3AdaBoost.M1 with CART(5) (AdaBoost.M1(5)) performed the same as its base classifier; increasing the number of boosting iterations did not decrease the error on the independent test set. On the contrary, AdaBoost.M1 with stumps (AdaBoost.M1(1)) improved the performance of its base classifier and the test error substantially decreased when combining more classifiers (increasing the number of boosting iterations).We identified the reason for the inability of AdaBoost.M1(5) to improve the performance of its base classifier in the mechanism used to obtain modified versions of the data to which the base classifier is applied. Recall that the weights applied to the training observations depend on the re-substitution error (see the “p to n ratio (data not shown). The error rate of stumps was non-zero even when the number of variables was large, however it was substantially smaller than the true error rate for this setting (0.5).We illustrate how the overfitting of CART depends on the high-dimensionality in a setting when there is no difference between the classes (test set error should be 0.5). The resubstitution error rate of CART(5) dropped to zero when the number of variables increased; zero resubstitution error rate was achieved faster when the sample size was smaller Fig. . We expeThese results indicate that AdaBoost.M1(5) could perform better than its base classifier when the sample size is larger, as the achieved resubstitution error rate is less frequently zero. More results about the effect of the sample size appear later in the paper.To avoid the problem arising from zero resubstitution error we propose to estimate the error rate achieved by the base classifier at each boosting iteration with cross-validation (AdaBoost.M1.ICV). This modification of the weights in the AdaBoost.M1 algorithm substantially improved its performance when using CART(5) as a base classifier Table , while iGradient boosting (GrBoost) has the potential of performing better than AdaBoost.M1 in our settings, since it produces classifiers that are not identical to its base classifiers, even when the training error of the base classifier is zero  Gradient boosting generally performed substantially better without shrinkage (ν=1).Similarly to AdaBoost.M1, when we used CART(5) with small samples , which was more obvious with less variables; with many variables GrBoost(5) behaved similarly to AdaBoost.M1(5).For the other algorithms, combining more classifiers (increasing the number of boosting iterations) resulted in smaller test set errors, but increasing the number of boosting iterations beyond 200 or 300 had only a marginal effect on the test set error. The most prominent exception was AdaBoost.M1.ICV, where more iterations were needed to achieve the smallest test set error when compared with the other classifiers.Overall, in these settings the best performance was obtained with St-GrBoost(1)  and St-GrBoost(5) (large number of variables).The test set error as a function of the number of boosting iterations decreased more when there were less variables. When we adjusted the differences between the classes so as to achieve approximately the same test set errors using CART(5), regardless of the number of variables, we observed that the gain from boosting was approximately the same in all the settings  and the number of variables. The results are shown in Fig. μ2=0.5, lower panels: μ2=1); exact numerical results as well as the results for the other performance measures are shown in Additional file In this section we investigate how the algorithms perform when the size of the training set is increased. We also varied the difference between the classes . The ability of AdaBoost.M1(5) to perform better than its base classifier depended on the sample size  performed the same as its base classifier with sample size smaller than 200 or 300), the number of variables ) and the difference between the classes . AdaBoost.M1.ICV(5) outperformed AdaBoost.M1(5) in the settings where the former did not perform better than its base classifier. It also performed well when there were more variables.Similarly as AdaBoost.M15), GrBoost(5) was unable to improve the performance of its base classifier when the sample size was small (50 samples), but its performance improved with larger sample sizes. The performance of Gradient boosting with stumps was better than with CART(5) when there were less than 100 samples, while the results were similar for larger sample sizes. Stochastic Gradient boosting performed much better than Gradient boosting in all the settings. The performance of St-GrBoost with CART(5) and stumps was very similar , St-GrBoost and AdaBoost.M1(5)  was able to improve the performance of its base classifier), it only marginally improved the performance of AdaBoost.M1(1) and GrBoost(5), while it did not yield any improvement for GrBoost(1) and LogitBoost . Generally, the optimal number of boosting iterations was smaller when there were more null variables; see also Additional file The classifiers were also ranked by their respective geometric mean of class-specific PAs , GrBoost(5) outperformed GrBoost(1) (data not shown). LogitBoost performed poorly in this setting and achieved substantially higher test set error than the other classifiers.In these settings AdaBoost.M1.ICV achieved much smaller test set error than AdaBoost.M1(1) and in general boosting algorithms using CART5) outperformed the algorithms using stumps  (which in this setting was unable to improve the performance of its base classifier), and LogitBoost (that suffered from overfitting), it was better to use 1000 boosting iterations instead of selecting its number by cross-validation; the former approach yielded smaller test set errors, which were only slightly larger than the optimal test set errors.The cross-validated number of iterations was generally much smaller than the optimal number of iterations; consequently, the test set errors were larger than the optimal (about 0.05 larger for 5-fold CV). Overall, LOOCV and 5-fold CV performed similarly. The best performance was obtained with LOOCV for Stochastic Gradient Boosting and with 5-fold CV for the other algorithms; however, the differences were large (>0.05) only for AdaBoost.M1(1).ntrain≤200, depending on the number of variables and the magnitude of the between class difference). A similar behavior was observed also for GrBoost(5), but this was limited to situations with a very small sample size (ntrain=50).AdaBoost.M1(5) performed similarly as its base classifier when the number of samples was small (AdaBoost.M1.ICV(5) outperformed AdaBoost.M1(5) in the setting with small sample size and performed similarly otherwise. The method performed similarly as AdaBoost.M1(1). We showed that AdaBoost.M1.ICV(5) can outperform AdaBoost.M1(1) using an example where the separation between the classes was complex.Shrinkage did not improve the performance of Gradient boosting, the only exception was the setting with a large sample size (500 samples) when using stumps as base classifiers. Shrinkage improved the performance of Stochastic Gradient boosting, which could in some settings  overfit when shrinkage was not used. However, the performance of Stochastic Gradient boosting was good and in general it was better than the performance of the other boosting algorithms.LogitBoost was likely to overfit when performing a large number of boosting iterations. However, even when using a small number of boosting iteration the performance of LogitBoost was poor when compared with the other methods, especially when the number of variables and the training sample size were large.Using cross-validation to determine the number of boosting iterations underestimated the optimal number of iterations needed to obtain the smallest test set error and the classification results were therefore suboptimal. With the exception of LogitBoost and GrBoost(5) it was better to use a fixed large number of boosting iterations instead of cross-validation.The boosting algorithms were used to reanalyze three breast cancer microarray gene expression data.We ranked the classifiers by their g-means, considering the highest g-means obtained with different number of boosting iterations Table . OverallAdaBoost.M1(5) achieved better classification results than its base classifier only for the Wang dataset (for the prediction of Relapse), which was expected as this was the largest dataset and the prediction task was hard  rather than a larger tree (CART(5) in our analyses). Our simulation results with thousand variables indicate that base classifiers stronger than stumps can be beneficial, not overfitting the training data, only if more than hundred samples from each class are included in the training set as in this case the overfitting of the base classifier is smaller.To overcome these problems we propose AdaBoost.M1.ICV, a modification of the AdaBoost.M1 algorithm that uses the cross-validated error rate for the update of the weights in the boosting algorithm. We show that in the high-dimensional setting with small samples AdaBoost.M1.ICV can outperform AdaBoost.M1 with larger trees as base classifiers. With AdaBoost.M1.ICV it is feasible to use base classifiers that would otherwise overfit the high-dimensional training data and impair the performance of boosting. This can be beneficial for the prediction problems that cannot be accurately addressed using very weak classifiers, as it was illustrated using data where the separation between classes was more complex.Gradient boosting suffers from problems that are similar to those outlined for AdaBoost.M1: they are less severe as they arise for smaller sample sizes, larger number of variables, bigger between classes differences. The reason why Gradient boosting performs poorly with small samples is that the class probabilities are in the high-dimensional setting severely overfitted, similarly as the resubstitution error used by AdaBoost.M1. As a consequence the decrease of the loss function is marginal and the updates to the final score are negligible. Similarly as observed for AdaBoost.M1, this problem can be diminished by using weaker base classifiers (stumps) as in this case the overfitting of the class probabilities for the training data is less severe. The prediction results of gradient boosting depend on the choice of the value of the shrinking parameter. In the low-dimensional setting shrinkage improves the performance of Gradient boosting  and it iν=0.01 works well in most settings.Stochastic Gradient boosting performed better than Gradient boosting and overall, it achieved the best performance among the algorithms that were considered. The reason for its better performance can be explained by the mechanism used to update the score in the gradient boosting algorithm: only a fraction of samples is used to train the base classifier, but the score update is based on all the samples. Therefore, the update depends partly on the data used to train the classifier and partly on the left out samples. In a way this strategy is similar to the AdaBoost.M1.ICV, where cross-validated estimates were used for the updates. It was observed that Stochastic Gradient boosting performs poorly without shrinkage on low-dimensional data . We obseFor low-dimensional data boosting was shown to be very robust to overfitting . Our resThe other algorithms do not suffer from overfitting even after performing 1000 iterations; importantly, performing more than few hundreds boosting iterations generally only marginally affects the test set error. AdaBoost.M1.ICV achieves the minimum test set error more slowly than the other algorithms, however after many boosting iterations the ICV approach performs similarly to best performing algorithms.The number of boosting iterations needed to achieve the best predictive performance varies substantially across different simulation settings, which suggests that the optimal number of boosting iterations should be estimated from the data for all the boosting algorithms. We addressed this issue by using cross-validation to determine the optimal number of boosting iterations. Our results indicate that this approach underestimates the optimal number of boosting iterations when the size of the dataset is small and, as a consequence, the classification performance of the algorithms is suboptimal.ntrain). We selected the smallest number of iterations in case of ties and therefore, the optimization based on a step function underestimated the optimum. In practice the problem is less important for larger sample sizes (data not shown) as in this case the function of the cross-validated error becomes more continuous. Other approaches for the determination of the optimal number of boosting iterations depend mostly on some likelihood based information criteria . InC or BIC . This suMease and Wyner  showed tOthers argued that larger trees can capture higher-level interaction effects among the variables, while stumps can only capture the main effects but perform best with boosting methods when the generative model is additive .Our limited exploration of the effect of the size of the trees on boosting performance showed that the overfitting of the base classifier hinders the performance of boosting and should be avoided or controlled (as discussed previously). Smaller trees are less prone to overfitting and are therefore preferable for AdaBoost.M1 and Gradient Boosting, unless the data are generated by a very complex model that cannot be accurately captured by combining simple base classifiers. For Stochastic Gradient boosting, which embeds some control for the overfitting of the base classifier, stumps and larger trees performed similarly when a large number of boosting iterations was performed: larger trees performed better with fewer iterations. This can easily be explained by noting that in our simulation study the differences between the classes were additive, i.e. we only considered main effects in our simulation study. Because of this, the differences between the classes could have been equally well explained by combining many classifiers where each considered only one variable or combining fewer classifiers where each considered more variables. However, we showed that also Stochastic Boosting can benefit from larger trees when the data generating process is complex.We did not systematically evaluate the effect of the actual tree size, as we only looked at stumps and CART with settings resulting in relatively small trees. However, we can reasonably expect that AdaBoost.M1 with large trees as base classifiers will not be effective in the high-dimensional setting, unless the sample size is very large, which is uncommon in the practical applications. We constrained the trees to be of a fixed size, as proposed by , rather In the simulation study we did not perform any type of variable selection, as it is embedded in the classification trees with a predefined size. For example, stumps use only one variable that gives the best split of the training set samples in two nodes, while in our implementation CART(5) used at most 31 variables . The results for the two-class prediction tasks presented in  show thaAdaBoost.M1 with large trees does not perform better than its base classifier when data are high-dimensional. We showed that large trees achieve perfect prediction of the training set samples even when there is no true difference between the classes, therefore the weights used in AdaBoost.M1 are not updated and the boosted classifier yields exactly the same prediction result as its base classifier. A similar issue is observed also for Gradient boosting when used with few training data. One way to diminish this problem is by using weaker base classifiers, i.e. smaller trees where the extent of overfitting is smaller. If the differences between the classes cannot be accurately captured by weak base classifiers, i.e. in settings with higher level interaction effects between the variables or complex boundaries, this problem has to be accounted for in the boosting algorithm. We propose a modification of AdaBoost.M1 algorithm where we use cross-validated error rate when updating the weights. This approach performs well in our simulation study and is robust to overfitting even when the number of boosting iterations is very large. Stochastic Gradient boosting can also avoid the problems arising from overfitting of its base classifier: only a fraction of the samples is used to train the base classifier, while the score updates are based on all the samples. Overall, Stochastic Gradient boosting with the shrinkage parameter set to a small value achieved the best classification performance in our simulation study as well as using real high-dimensional microarray data.


Describe the diagnoses and the time to recovery of running-related injuries in novice runners.Prospective cohort study on injured runners.This paper is a secondary data analysis of a 933-person cohort study (DANO-RUN) aimed at characterizing risk factors for injury in novice runners. Among those sustaining running-related injuries, the types of injuries and time to recovery is described in the present paper. All injured runners were diagnosed after a thorough clinical examination and then followed prospectively during their recovery. If they recovered completely from injury, time to recovery of each injury was registered.A total of 254 runners were injured. The proportion of runners diagnosed with medial tibial stress syndrome was 15%, 10% for patellofemoral pain, 9% for medial meniscal injury, 7% for Achilles tendinopathy and 5% for plantar fasciitis. Among the 220 runners (87%) recovering from their injury, the median time to recovery was 71 days .Medial tibial stress syndrome was the most common injury followed by patellofemoral pain, medial meniscal injury and Achilles tendinopathy. Half of the injured runners were unable to run 2×500 meters without pain after 10 weeks. Almost 5% of the injured runners received surgical treatment. Running-related injuries are common in novice-, recreational- and elite runners. According to a recent systematic review by Lopes et al In novice runners, however, limited insight is available in the literature into the type of running-related injuries. Buist et al In a recent prospective cohort study entitled the Danish Novice Running project (DANO-RUN) The DANO-RUN study was a 1-year prospective follow-up study aiming at characterizing risk factors for injury in novice runners. Present paper describes a sub-analysis of the injured novice runners included in the DANO-RUN study. The procedure for enrolment, inclusion and exclusion criteria and main purpose of the DANO-RUN study has been presented elsewhereA novice runner was defined as a person who had not been running on a regular basis for the past year. The cut-off to define a regular basis was set at 10 km of the total running distance in all training sessions during the past year prior to inclusion. If a total of 10 km was exceeded a person was ineligible for inclusion. For instance, a person was included if he/she had been running a total of 3 times 2 kilometers in the past year and excluded if he/she had been running 5 times 4 kilometers. A running-related injury was defined as any musculoskeletal complaint of the lower extremity or back caused by running, which restricted the amount of running  for at least 1 week. This definition of injury was a modified version of the injury definition used in the studies on novice runners Prior to the study, no consensus-based definition of time to recovery was found. The authors defined recovery from injury as no pain in the affected anatomical location following two consecutive running sessions of at least 500 meters. The time to recovery was calculated as time in days from injury occurrence to complete recovery. In the case a participant was free of pain in activities of daily living but refused to run in order to evaluate on their symptoms, the recovery was defined as the day they were free of pain.All injured participants attended a clinical examination in case of injury. At the examination, the participant was examined and diagnosed, preferably no later than 1 week after the participant had requested an examination. In most examinations (more than 80%), at least two physiotherapists (of four assisting in the study) diagnosed the injured participant based on a consensus agreement. A standardized examination procedure was used in each of the following anatomical locations: foot/ankle, lower leg, knee, thigh, hip and back. Furthermore, guidelines for diagnostic criteria were used to classify the injuries into specific diagnoses/types of injuries. These non-validated guidelines were developed by the DANO-RUN research group prior to the study . At eachIn case the physiotherapist was unable to diagnose the injured runner at the clinical examination or the participant did not recover as expected after being diagnosed, an additional examination including diagnostic imaging (most often MRI) was performed. Such examination was provided in approximately 25% of all injuries and was always offered if the physiotherapist at the first examination diagnosed injuries like medial meniscal injury, osteoarthritis or stress fractures. The additional examination and diagnostic imaging were performed at the Division of Sports Traumatology at Aarhus University Hospital, Denmark. Based on the clinical examination(s), the diagnoses were registered for all injuries occurring from inclusion in the study and in the following 1-year period. In cases where the examiners  were in doubt and the diagnostic imaging was negative, the injury was classified as unknown.After the clinical examination, all injured participants were followed prospectively and contacted by phone or mail once every 2 to 3 weeks to follow-up on injury status. In case the participants recovered from injury, questions regarding the use of medication (yes/no), treatment assistance from health professional (yes/no), surgical treatment (yes/no) and missed days from work (number) were asked. In addition, participants had to report their motivation to start running again after having had an injury and this information was then dichotomized into: 1) Less motivated or not at all motivated or 2) motivated or very motivated. If a participant completely recovered from injury and sustained an additional running-related injury afterwards, the participant had to attend a clinical examination again. The possibility of clinical examinations was stopped after the participants had been included in the DANO-RUN study for 1-year. The follow-up on injured participants were stopped February 2013, eighteen months after the first participants were enrolled at a baseline investigation and 7 months after the last participants completed follow-up.t-test was used to test if time to recovery (days) was different between those motivated to take up running again after having had an injury compared with those being less motivated. The Wilcoxon rank sum test was used to test if the time to recovery was different across gender and across age  and 18 had an average distance above 10 kilometers.The proportions of runners sustaining different types of injuries are presented in A majority of the injuries occurred in the lower leg , knee  and ankle/foot , followed by the hip . Only a minority of injuries occurred in the upper leg  and lower back . Finally, four injuries (1.5%) occurred in other anatomical locations than the above.In p = 0.06 than those motivated to start running after having had an injury.Among those recovered from their injury, ten runners missed days from work because of their injury (range 1 day to 14 days). The diagnoses which caused missed days at work were mainly medial meniscal injury (n = 3) and stress fractures (n = 3), but also ankle inversion injury (n = 1), anterior compartment (n = 1), Achilles tendinopathy (n = 1) and gastrocnemius injury (n = 1). Among 203 participants reporting information about their motivation to start running after full recovery, 22 participants were less motivated or not at all motivated to start running again. These participants had an almost significantly longer time to recovery (log transformed) of 0.36 days , Medial tibial stress syndrome was the most common injury among novice runners followed by patellofemoral pain and injury in the medial meniscus. Depending on injury type, injured novice runners should expect a median time to recovery of approximately 10 weeks. Of the 933 healthy novice runners originally included in the DANO-RUN study, 4% remained injured  at the end of follow-up and had to permanently stop running because of their injury.To our knowledge, the present study is the first to provide insight into the time to recovery for all types of running-related injuries. Based on the results presented in The 254 injured runners described in the present paper were a sub-sample of the 933 volunteers included in the DANO-RUN study A major strength in the present study is the prospective approach used to gather information about the runners and their injuries. Previously, retrospective studies have been conductedAn additional strength was the use of an early clinical examination to diagnose each injury. In many studies, participants report their injury by mail or telephone without attending an examinationAt least three major limitations in the present paper must be considered: the purpose of the study, the possibility for information bias based on a clinical examination to diagnose a running-related injury and the injury definition.The purpose of the present paper was different than the original purpose of the DANO-RUN study. The study originally aimed at investigating if the time to injury occurrence varies among runners with different foot postures and progressions in training distance. Additionally, the role of demographic characteristics and behaviour on injury development has been investigated based on the same dataset. Results from these analyses have been presented elsewhere Another weakness may be the approach used to diagnose injuries. In the present study, most injuries were diagnosed based on the patient history and by performing a physical examination without the use of diagnostic imaging. Previously, Khan et al. The injuries occurring in the present study was defined as any musculoskeletal complaint of the lower extremity or back caused by running. It can be argued, that some of the injuries included were not directly caused by running. For instance, knee osteoarthritis and injury in the medical meniscus may be caused by other factors and running leads the injuries/conditions to become symptomatic. Since all participants in the study reported themselves as healthy at baseline, such injuries can only be excluded at baseline by performing a diagnostic imaging of all subjects prior to inclusion. This procedure was not used in the present study because of financial reasons.In addition to the weaknesses presented, it must be stressed that the participants included in the present study had to run in a neutral running shoe. It, therefore, remains unclear if the types of running-related injury are different among novice runners using other types of conventional shoes or minimalist shoes.In the present study, medial tibial stress syndrome was the most common injury diagnosed in 38 of 254 injured beginners (15%). In a review by Lopes et al Material S1Non-validated guidelines to classify the injuries into specific diagnoses/types of injuries.(DOCX)Click here for additional data file.


Gene therapy with adeno-associated virus (AAV) has advanced in the last few years from promising results in animal models to >100 clinical trials (reported or under way). While vector availability was a substantial hurdle a decade ago, innovative new production methods now routinely match the scale of AAV doses required for clinical testing. These advances may become relevant to translational research in the chronic pain field. AAV for pain targeting the peripheral nervous system was proven to be efficacious in rodent models several years ago, but has not yet been tested in humans. The present review addresses the steps needed for translation of AAV for pain from the bench to the bedside focusing on pre-clinical toxicology. We break the potential toxicities into three conceptual categories of risk: First, risks related to the delivery procedure used to administer the vector. Second, risks related to AAV biology, i.e., effects of the vector itself that may occur independently of the transgene. Third, risks related to the effects of the therapeutic transgene. To identify potential toxicities, we consulted the existing evidence from AAV gene therapy for other nervous system disorders  and from the clinical pharmacology of conventional analgesic drugs. Thereby, we identified required preclinical studies and charted a hypothetical path towards a future phase I/II clinical trial in the oncology-palliative care setting. Unrelieved chronic pain is a critical health problem in the US and worldwide. A report by the Institute of Medicine estimated that 116 million Americans suffer from pain that persists for weeks to years, with resulting annual costs exceeding $560 million ,3. AntinTwo vector systems have been most extensively studied in gene therapy for pain: Herpes simplex virus (HSV), which has already been tested in cancer pain patients ; and adesin transgene), i.e., viral and immune-biology (section 4); and the specific therapeutic gene, i.e., pharmacology (section 5). An overview is provided as a semantic “mind map” in Figure The potential toxicities of AAV for pain can be conceptually broken down into mutually exclusive categories of risk. This review divides toxicities as follows: the procedure employed for vector delivery to the target tissue, i.e., a surgical/interventional risks (section 3); the AAV vector itself  or intraganglionic (IG) administration.cisterna magna and advancing it caudally to the lumbar level [The IT route delivers the vector to the cerebrospinal fluid (CSF) and has been achieved in rats by inserting an IT catheter through the ar level -10. The ar level ,12. In par level . In humaar level .Therefore, no additional toxicity studies would be needed in regards to the LP procedure risk, if a phase I trial of AAV for pain were to test vector administration by the IT route.The IG route delivers AAV directly into the DRG parenchyma. IG studies in rats accessed the DRG by an open neurosurgical procedure -19, whicIn the field of interventional neuroradiology, it is common practice to target small structures such as the DRG for drug injections or biopsy. Ultrasound ablation of the spinal sensory nerve root has also been reported . TherefoClinical translation of AAV for pain faces the issue of general vector toxicity: the risk of AAV itself . This section is predicated on the conservative assumption that vector may spread systemically to all organ sites, even though the IT and IG route of administration attempts to limit such spread.Previous experience with AAV gene therapy identified several mechanisms by which the vectors trigger an adverse reaction; each is addressed in a separate subsection below.AAV vectors persist in cells in two states, as episomal concatamers and, for a small minority of vector copies, chromosomal integration . In a spTransmission to the germ line has not been found in any AAV clinical trial. While AAV particles were detected in the semen of subjects receiving AAV2 into the hepatic artery , this wain vitro in the setting of co-infection with the wild type AAV2 and a helper virus [The potential of replication escape of the recombinant AAV was shown er virus . HoweverThe most serious adverse event in the setting of a AAV human trial occurred in 2007 when a patient died 22 days after receiving intra-articular AAV encoding etanercept, a tumor necrosis factor receptor blocker ,29, fromIn regards to AAV for pain, the above evidence suggests that issues of insertional mutagenesis, germ line transmission, replication escape, and dissemination of the vector to the public domain, have been so thoroughly addressed in the field. The adverse event in the AAV-etanercept trial suggests that in the case of IL-10, a transgene that is similar to etanercept in regards to its immune-suppressive effect, measurement of systemic levels should be an important goal of preclinical toxicity assessments. Furthermore, a pain trial in patient with incurable cancer could answer some of the above questions in humans, at least if (some of the) patients may decide that after their  death from cancer  autopsy tissue may be sampled for research purposes.AAV for pain by the IT or IG route will trigger a serological immune response to the vector capsid. In addition, cellular immunity to the vector capsid has to be carefully considered, although (or because) fewer predictions may be possible in regards to the cellular immunity one may anticipate in humans.AAV delivered IT or by direct infusion into the brain parenchyma led to >100-fold increase in the titer of circulating NAb in large animal models as shown for AAV2 ,31, AAV1The presence of pre-existing circulating NAb inhibits AAV transduction when the vector is delivered systemically . In regaFor the development of AAV for pain, we conclude that the vector will induce NAb without any pathological consequences. Pre-existing NAb may diminish IT transduction rates, but the extent or threshold is a clinical research question (of minor importance) that may be answered in a clinical trial. Pre-existing NAb will not affect IG delivery of AAV. Taken together, we propose that a phase I trial of AAV for pain should collect specimen to measure NAb before and after vector administration, while no further preclinical studies are required towards that plan. Preexisting NAb should not be an exclusion criterion for individual patients considered for IT or IG AAV for cancer pain.The clinical significance of CD8 mediated cytotoxicity directed against the AAV capsid and leading to a destruction of the transduced cells arose as an issue in a clinical trial of AAV2 for hemophilia B . ImportaSubsequent animal and human studies demonstrated that the extent of cellular immune response against AAV might depend on the host species, the AAV serotype, and the target tissue. A comparison of rodents, large animal species, and humans showed that humans are prone to mount a cellular immune response to systemic AAV more readily than other species ,47. The post-mortem histopathological analysis for possible immune-toxicity. This approach appears particularly justified by the absence of cellular immunity-associated adverse effects in the context of previous AAV studies in the CNS. Furthermore, a cellular immune response could be effectively managed by immunosuppression [Based on this evidence, the occurrence of a cellular immune response against the AAV capsid in the PNS cannot be definitely excluded on the basis of large animal studies. It will therefore be a critical focus of a phase I clinical trial of AAV for pain, which will need to include collection of peripheral mononuclear blood cells and extensive pression ,49, suggIn order to avoid toxicities and facilitate clinical translation of AAV for pain, selection of AAV serotypes will play a critical role. The ideal serotype for AAV for pain would effectively transduce PNS neurons after IT or IG administration; show low levels of escape to peripheral organ sites; and have precedent safety data available from a clinical trial (for another disease).AAV8 effectively transduced the DRG neurons, while showing limited brain penetration upon IT administration in rodents -61, whic13 genome copies (GC) in NHP (with projected dose of 3.5 × 1013 GC in humans). Widespread CNS transduction may be a characteristic of AAV9 and may appear less attractive for gene therapy for pain, at least for the current approaches that specifically target the PNS and spinal cord. AAV9 has also been reported to transduce the CNS after intravascular delivery, suggesting the ability of this serotype to cross the blood brain barrier (BBB) [IT AAV9 transduces both the PNS and CNS ,44,62,63er (BBB) ,44.12 GC of AAV2 to the brain has been demonstrated in the setting of Canavan disease [11 GC, while achieving higher brain penetration after intraparenchymal infusion compared to AAV2 [AAV2 and AAVrh10 are the only serotypes that have been clinically tested in humans in the CNS. The safety of intraparenchymal infusion of up to 1 × 10 disease , late in disease , Parkins disease ,41,65-67 disease . AAVrh10 to AAV2  and was  to AAV2  and LINC to AAV2 .Recent advances in AAV biology showed promise in developing AAV particles with improved transduction efficiency or tissue tropism. The improved transduction efficiency has been demonstrated by using self-complementary (sc-) AAV vectors  and whilIn regards to AAV for pain, we would arrive at different conclusions for the two delivery routes under consideration: IG administration of AAV2 or AAVrh10 as well as IT administration of AAV9 might not need any further investigations of serotype-specific toxicities and might only need to be further assessed in regards to the toxicities of a delivered therapeutic gene (as detailed in Section 5 below). IT AAV8 requires further biodistribution studies to confirm whether its feasible characteristics observed rodents also apply to large animals, which will then determine whether AAV9 or AAV8 would the vector of choice for the IT route in the clinics.In this section, the review will focus on two transgenes that appear particularly promising for translating AAV gene therapy for pain: prepro-ß-endorphin (ppßE) and interleukin-10 (IL-10). The therapeutic efficacies of these two genes have been investigated and validated in multiple vector systems including AAV by several independent groups including ours ,72-74. TEffects of the transgene products can be assessed by applying the principles established in pain pharmacology for conventional (non-gene therapy) drugs. Most analgesic drugs, including the peptide ß-endorphin , show dose dependence in regards to both their analgesic- and toxic effect. Low doses of analgesic drugs thereby exert low (if any) pharmacological effect and dose escalation results in an analgesic effect and eventually, if doses exceed a certain threshold, the gradual emergence of toxicity. The drug level needed to produce the toxic effect relative to a therapeutic effect is the therapeutic index.The emphasis on the dose–response relationship and the therapeutic index in pain pharmacology provides important guidance in regards to the development of AAV for pain. A certain degree of toxicity may be expected at high-therapeutic doses and may be not only acceptable but even the goal of dose-escalation in order to optimize the therapeutic benefit. The rationale for delivering the ppßE gene by AAV is the expectation of a favorable therapeutic index. The basis for its safe development will be the appropriate choice of the initial vector dose in a phase I/II trial.Conventional analgesic drugs, such as opioids, administered orally or intravenously expose all sites of the body to similar drug concentrations. In contrast, AAV gene therapy for pain by the IT or IG route aims at targeting anatomical sites known to mediate analgesic efficacy of opioids but not toxicity: the spinal compartment of the nervous system including the DRGs and the posterior horn of the spinal cord.IT delivery improves the therapeutic index of conventional opioids. This principle is firmly established through animal studies ,12 and tAn important observation in the field of AAV for pain, however, has been the marked tropism of AAV for DRG neurons if administered IT or IG. This was a very favorable, while unexpected, finding and has subsequently been confirmed by several groups in different animal models -61,80-82Of note, a high degree of DRG-neuron selectivity has been the fundamental rationale for the HSV-based approach to pain gene therapy by the subcutaneous route of vector administration ,83. IT AßE, the secreted peptide product of ppßE, is a μ-opioid receptor agonist and thereby shares its pharmacological activity with the most commonly used conventional opioids such as morphine. The toxicological assessment of ppßE can therefore be guided by the robust clinical experience with exogenous opioids. In addition, ßE was tested in clinical trials.The principal sites where opioid toxicity occurs at high doses are the CNS and the periphery. The most common adverse effects of opioids in the CNS are nausea and vomiting, mental status changes, pruritus, urinary retention, and respiratory depression -87, whilWhen IT administration of ß-endorphin was tested in humans, it resulted in profound analgesia similar to that attainable with exogenous opioids -97. DoseIn conclusion, the therapeutic and toxic effects of ppßE are expected to be vector dose-related and the specific toxicities can be predicted from the previous clinical experience with IT opioids and ßE. What remains to be defined in the preclinical large animals studies is the maximum tolerated dose (MTD) of the vector encoding ppßE for both the IT and the IG route. A phase I/II clinical would then test safety for vector doses starting with the lowest potentially efficacious dose and sequentially being escalated towards the MTD established in animals.IL-10 is of interest for pain therapy because it may be the first/only available agent to target a mechanism of chronic pain first described about a decade ago, glial activation . RecombiWhile toxicity of IL-10 delivery targeted to the PNS by either the IT or the IG route has not been addressed in large animal models, rodent data suggest good tolerability of IL-10 in the CSF ,101,102.Establishing the MTD for AAV encoding IL-10 will be an important goal for both the IT and the IG route. Clinical testing would then start at a lower vector dose with subsequent dose escalation to establish the MTD in humans.Syngeneic transgenes will not induce serological or cellular immunity. Therefore, no immunity is to be expected in a clinical trial using human IL-10 as therapeutic gene and no special studies appear to be necessary towards assessing it.The case of ppβE may require additional consideration, because the gene includes an artificial fusion site. Specifically, the pre-pro sequence of ppβE  is taken from nerve growth factor (NGF); thus, this portion of the gene by itself is not of concern because it can be made syngeneic (by taking the pre-pro sequence from human NGF). The carboxy terminal portion of ppβE gene encodes the pharmacologically active βE peptide. By itself this portion of the gene is not of concern either, because it can also be made syngeneic (by using the βE sequence as it occurs in the precursor protein human pro-opiomelanocortin (POMC)). In the ppβE gene, these two syngeneic peptides are fused creating a potentially non-syngeneic epitope.via the constitutive secretory pathway yielding continuous release of the pharmacologically active βE peptide, which is the desired property of this therapeutic gene. Because the processing of the ppβE peptide into its syngeneic components, the pre-pro-sequence and βE, occurs immediately following its ribosomal synthesis and co-translational transport into the ER, the fusion epitope may not even have a chance to become immunologically apparent. Therefore, to evaluate the possibility of cellular immune toxicity, pre-clinical toxicity studies of ppβE will have to assess the integrity of transduced tissues such as neuropathological evaluation of DRG.The rationale for using ppβE rather than POMC is to circumvent the requirement of POMC to be processed in acidified secretory vesicles found only in certain cell types and its reliance on the regulated secretory pathway. ppβE can be processed into βE by all cell types and secretion of βE occurs A challenge for pre-clinical toxicology assessments is whether to perform animal testing with syngeneic transgenes, i.e., specifically adapted to the animal species employed, or to perform testing of the human-type vector slated to be used in a clinical trial, i.e. xenogeneic to the animal model. Xenogeneic transgenes are well-tolerated in rodents, which express even the bacterial EGFP gene long term. However, xenogeneic transgenes induced immunity in large animals . As a coResponsible and effective preclinical-to-clinical translation of AAV for pain represents a substantial scientific and regulatory challenge. Yet, the goal appears to be supported by two considerations: The growing clinical research experience with AAV (for non-pain applications) and the need for innovative clinical research in patients with intractable pain from incurable cancer. The following two sections follow these two notions in an attempt to turn our review findings into a proposed action plan.For the IT route of AAV delivery, preclinical animals studies are needed to● Establish the MTD of AAV vectors◦ Assess health of animals clinically◦ Neuropathological assessment of neural structures such as the DRG◦ Repeat independent experiments for each specific vector design: transgene, serotype11 to 1013 GC, then test up to 1014 GC in a large animal◦ If MTD cannot be reached, document absence of toxicity at a dose exceeding the highest concentration anticipated in humans, e.g., if dose range contemplated in humans is 10● Establish biodistributionWe propose that testing in large animals would be more meaningful than testing in rodents, even if performed in a significantly smaller number of animals, because a species such as the pig better resembles the mechanical-anatomical and immunological characteristics of humans than rats. We propose that testing for the first transgene-capsid combination should be done in two iterations: a long-term experiment (such as 3 months follow up) with the syngeneic transgene (such as made for the pig) and a short-term experiment (such as ten days) with the human transgene (that would be xenogeneic in pigs). For subsequent assessments of other transgene-vector combinations, it may be justified to limit studies to the vector that is to be used in the clinical trials expressing the human transgene version.For the IG route of AAV delivery, additional preclinical animal studies are needed to● Confirm the safety of placing a CED needle into the DRG for vector infusionExisting studies reviewed above have already addressed the following issues:per se appears non-hazardous● General vector related toxicities including insertional mutagenesis, germ line transmission, replication escape, and dissemination of the vector to the public domain—AAV vector administration sin transgene)● Serologic response against AAV . Enrollment requirements could include a minimum- and a maximum life expectancy. Both could be shorter for early patient cohorts to limit the impact that unexpected adverse effects could have, such as a range of 4 to 8 weeks. As safety is established throughout the course of the trial, the minimum life expectancy could become longer, e.g., 3 months, to allow collection of data on longer-term effects. While the same high scientific standards should apply to a palliative-care clinical trial in cancer patients as to trials in patients with non-malignant diseases, the proposed clinical trial setting may be the most appropriate for a pre-clinical to clinical research transition in a case such as AAV gene therapy, where toxicities cannot be modeled perfectly in animals.A phase I/II clinical trial would likely focus first on the transgene with the best-known pharmacology, i.e., an opioid. ppßE may therefore be the most suitable candidate for early clinical translation thanks to the extensive clinical experience with exogenous IT opioids. In addition, the spread of the vector suspension in the IT space for a given volume can be guided by the experience with IT drugs such as methotrexate, which distribute within the CSF-filled space and are routine given as in a volume of 5 cc and may be safe for injection volumes of up to 10 ml in adults.An exciting medium- to long-term impact of studying AAV for pain in the cancer center setting may be the opportunity to gain insights into AAV biology in humans. Given that some patients may consent to postmortem tissue collection (an autopsy) a trial in this setting may offer a unique chance for analyzing tissue samples from human subjects receiving AAV at a markedly more rapid turnaround than in previous AAV clinical trials.Another long-term potential from the perspective of analgesic treatment development is that AAV gene therapy may serve as a platform for investigating novel analgesic targets. For instance, the efficacy of IL-10 would test the analgesic effects of counter-acting glial activation in the setting of the human pain state, which could then guide development of conventional  drugs harnessing the same mechanism of action. While IL-10 has been proposed as an analgesic strategy more than a decade ago, clinical testing could not be performed to date presumably because prolonged IT delivery of recombinant IL-10 is impractical. AAV could be the means to bring novel analgesic strategies such as IL-10 from the laboratory into clinical trials.The authors declare that they have no competing interests. RJS is a founder of Asklepios Biopharmaceutical, Inc.JP and ASB wrote the manuscript. LFH prepared the figure. CHE, AA, and RJS contributed sections on adeno-associated viral vectors. All authors read and approved the final manuscript.


Hair cycle disturbances are common in dogs and comparable to some alopecic disorders in humans. A normal hair cycle is maintained by follicular stem cells which are predominately found in an area known as the bulge. Due to similar morphological characteristics of the bulge area in humans and dogs, the shared particularity of compound hair follicles as well as similarities in follicular biomarker expression, the dog is a promising model to study human hair cycle and stem cell disorders. To gain insight into the spatial distribution of follicular keratinocytes with stem cell potential in canine compound follicles, we microdissected hair follicles in anagen and telogen from skin samples of freshly euthanized dogs. The keratinocytes isolated from different locations were investigated for their colony forming efficiency, growth and differentiation potential as well as clonal growth. Our results indicate that i) compound and single hair follicles exhibit a comparable spatial distribution pattern with respect to cells with high growth potential and stem cell-like characteristics, ii) the lower isthmus (comprising the bulge) harbors most cells with high growth potential in both, the anagen and the telogen hair cycle stage, iii) unlike in other species, colonies with highest growth potential are rather small with an irregular perimeter and iv) the keratinocytes derived from the bulbar region exhibit characteristics of actively dividing transit amplifying cells. Our results now provide the basis to conduct comparative studies of normal dogs and those with hair cycle disorders with the possibility to extend relevant findings to human patients. In the dog, like in humans and mice, hair follicles (HFs) self-renew and undergo recurrent phases of controlled growth (anagen), regression of the lower portion of the follicle through apoptosis of keratinocytes (catagen), and relative quiescence (telogen) . After aSCs are “slow-cycling”, relatively undifferentiated cells with high proliferative potential –4. In moin vitro which reflects the growth potential of cells in a given population. In addition Barrandon and Green defined different colony types derived from human epidermal keratinocytes, namely holoclones, meroclones and paraclones [SCs can be identified by their colony-forming efficiency raclones . Holocloraclones . In a laraclones , 10. The2 [2. Therefore we suggested that colonies with highest growth potential are small colonies with slow cycling cells [in vivo.In principle, SC-bearing colonies of human, mouse and canine origin have been reported to be larger than 3mm2 , 16–17. ng cells , which iAs outlined above, dogs and humans share many common HF features such as morphological similarities of the bulge area, similarities in hair types, anagen driven HCs (at least for some dog breeds), resemblance in marker expression , 18 as wTo reach our goal we investigated the growth potential of keratinocytes derived from compound and single canine HFs in healthy dogs by 1) assessing the colony forming efficiency of cells derived from defined locations of HFs; upper isthmus, lower isthmus and bulbar region, 2) investigating the growth potential compared to premature differentiation of resulting colonies using proliferation and differentiation markers; 3) comparing colony growth during different HC stages  and 4) investigating clonal expansion of canine follicular keratinocytes. In contrast to work done so far on canine follicular SCs, we assessed compound HFs in our study, included HFs in the telogen hair cyle stage and, to localize potential canine SCs more precisely within the HF, subdivided the isthmus further into two compartments (upper and lower isthmus) , 10. FurSkin samples were obtained from 29 freshly euthanized dogs of various breeds with no skin abnormalities. All dogs were privately owned pets that were euthanized according to ethical standards of the Canton Bern, Switzerland for reasons unrelated to this study. Samples were taken after euthanasia with the consent of their owners and were stored in Dulbecco’s Modified Eagle Medium (DMEM)  supplemented with 10% bovine serum and 1% penicillin-streptomycin-fungizone  at 4°C for no more than 16h prior to microdissection.CELLnTEC-09 medium (CnT-09) was purchased from CELLnTEC, Bern, Switzerland and prepared according to the instruction manual. To reduce microbial contamination, 1% penicillin-streptomycin-fungizone  was added.5 cells/ 35mm petri dish on a coverslip (18mmx18mm). After 2hrs cells were adherent to the cover slip and treated with mitomycin c for 2hrs at 37°C. Thereafter the 3T3 cells were maintained in DMEM supplemented with 10% bovine serum and 1% penicillin/streptomycin/fungizone until they were used as feeder cells.3T3 fibroblasts  were seeAs described previously , the bioThe colonies growing on the cover slip were stained with Ki67 ) and involucrin ) antibodies as described previously [2 and > 3mm2. The total number of colonies as well as the number of the three resulting colony types . Colonies ≥400μm in diameter were counted and divided in colonies < 3mm2, smooth perimeter) [Clonal expansion of colonies was performed for each location within the telogen  and the anagen follicle  in five and 12 dogs, respectively. One colony with holoclone-like morphology (>3mmrimeter)  was pickrimeter)  was pickP = 0.05) and rank sum test (P < 0.0167), respectively. All statistical analyses were performed using the software package Number Cruncher Statistical System 2008 .The total number of colonies as well as the number of the individual colony types were statistically evaluated for all dogs using the Kruskal-Wallis ANOVA  and telogen (seven dogs) HC stages with comparable results. Shown are results derived from experiments with follicular compounds.Overall, there was a substantial variation in the number of colonies derived from follicular compounds in both the anagen and telogen HC stage between the individual dogs. The total number of colonies ranged from 0 up to 78 in anagen compounds and from 0 to 73 in telogen compounds (data not shown). The majority of the HFs taken in duplicates from the same dog as single and compound HFs yielded a comparable number of colonies. In a few dogs the number of colonies also varied between the duplicates .The total number of colonies per cover slip derived from follicular keratinocytes of the bulbar region was significantly higher than from keratinocytes derived from the upper and lower isthmus  with meaLike in the anagen HC stage the number of colonies was higher from keratinocytes derived from the lower isthmus than from the upper isthmus , however, the difference was not statistically significant  compared to the colonies derived from the upper and lower isthmus, respectively (2 . Strikin2 (2 .In contrast to the colonies derived from keratinocytes of the anagen compounds the colonies derived from the telogen compounds had more involucrin positive colonies in the upper isthmus than in the lower isthmus (2 (2 (Like in the anagen follicle the majority of proliferative colonies (Ki67+) were smaller than 3mmAnagen hair cycle stage: Clonal growth of colonies from a single cell isolated from holoclone-like (larger than 3mm2 with a smooth perimeter) [rimeter)  primary Telogen hair cycle stage: Clonal growth of colonies derived from a single cell was examined for five dogs. In 6.0% and 18% of all seeded single cells derived from the upper isthmus and the lower isthmus, respectively, clonal growth of colonies occurred and colonies were of the mixed type (both Ki67 and involucrin positive).Anagen hair cycle stage: As assessed for 12 dogs, the majority of the colonies derived from the upper isthmus could be passaged once (45.4%). Almost a third of the colonies could be passaged twice (27.3%) and 9.1% of the colonies could be passaged three times. 18.2% of the colonies could not be passaged at all. The majority (54.5%) of the colonies derived from the lower isthmus could be passaged twice, 27.3% of the colonies could be passaged once and 18.2% could be passaged three times. The majority (44.4%) of the colonies derived from the bulbar region could be passaged once, 33.3% of the colonies could be passaged twice, 11.1% of the colonies could be passaged three times and 11.1% of the colonies could not be passaged at all. On average, the keratinocytes could be passaged 1.3, 1.9 and 1.8 times when derived from the upper isthmus, the lower isthmus and the bulbar region, respectively.Telogen hair cycle stage: For the five dogs, 25% of the colonies derived from the upper isthmus could be passaged once, twice and four times, respectively. 25% could not be passaged at all. The colonies derived from the lower isthmus could be passaged either once (50%) or four times (50%). On average, the keratinocytes could be passaged 1.75 and 2.5 times when derived from the upper and the lower isthmus, respectively.The goal of this study was to gain insight into the spatial distribution follicular SC in the canine HF to lay the basis for the elucidation of the pathogenetic mechanisms resulting in primary alopecia in dogs and humans. We defined the relative growth potential of follicular keratinocytes from different locations of the HF and from the main HC stages (anagen and telogen). Furthermore, our interest was in compound HFs, primarily because the compound structure is a special feature, distinguishing the canine and human hair coat from the mouse fur (composed of single HFs only). Compound HFs have not been investigated so far and in canine non-inflammatory alopecia, the secondary HFs of the compound HF are often retained in the kenogen HC stage . PreviouIn accordance with our findings previous studies showed that K15- and CD34-positive cells  are present mainly in the lower half of the isthmus in telogen , 18. How2 and thus showed no holoclone-like morphology. We assume nevertheless that these colonies are the SC- or early progenitor cell-bearing colonies because of their proliferative capacity. This observation suggests that the growth behaviour of canine SCs and early progenitor cells are different from that of mouse and human. They reflect however the growth behaviour of SCs in these species in vivo. The definition of “holoclones” originates from colony-forming human epidermal cells. However previous studies [In our study the colonies that were mainly Ki67+ were smaller than 3mm studies  describe studies  we also 2. Despite the large size of the colonies we identified these colonies as terminally differentiated based on their staining properties (involucrin+). Thus, the high number and the size of the colonies growing from bulbar region may reflect the high proliferative potential of matrix cells, which is in agreement with the in vivo situation.In our study a significantly higher number of colonies were growing from cells of the bulbar region in comparison to the upper and lower isthmus in the anagen HC stage. In addition these colonies had an irregular perimeter and large diameter bearing large cells. This finding is in contrast to published results from rats and human where only very few colonies could be grown from cells of the bulbar region –23. FurtThe statistical assessment of differences in terms of colony formation and colony subtypes between different locations and different HC stages, respectively, was difficult due to the uneven distribution of the data between the individual dogs and sometimes even between duplicates from the same dog. The striking differences between the individual dogs reflects that not only the intrafollicular microenvironment but also extrafollicular factors such as the molecules derived from the dermis or the intra-dermal adipose tissue, the general physiological status  as well environmental factors influence the HF and SC homeostasis stage . HoweverIndependently of the location of the HF, follicular colony forming cells only underwent a small number of generation doublings. This stands in contrast to the results from Rochat et al., where human follicular colony-forming cells underwent a high number of generations. However, Rochat et al. transferred colonies 7 days after seeding . PreviouDespite the fact that we could not achieve many passages the colonies derived from the lower isthmus could be passaged slightly more often than the colonies derived from other locations (in the anagen as well as in the telogen HFs), indicating that the lower isthmus is the region with the greatest SC potential. This result is in line with previous results in humans , where mTaken together our results indicate that in canine compound HFs the cells with the greatest SC potential are located in the lower isthmus in the anagen as well as in the telogen HC stage. Interestingly, we found the colonies with the greatest proliferative potential to be rather small and with an irregular perimeter. Therefore, we suggest that unlike in other species, canine SC-bearing colonies show no “classical” holoclone morphology.Our findings are important to further investigate molecular aspects of the canine HF SC compartments in health and disease, which might be a promising model for comparative studies with human diseases.S1 FigHair follicles were microdissected in three fragments: Upper isthmus, lower isthmus and bulbar region. Inf, infundibulum; UpIst, upper isthmus; LoIst, lower isthmus; Bulb, bulbar region.(TIF)Click here for additional data file.S2 FigHair follicles were microdissected in two fragments: Upper and lower isthmus. Inf, infundibulum; UpIst, upper isthmus; LoIst, lower isthmus.(TIF)Click here for additional data file.