Scientific advances are continually improving the knowledge of acne and contributing to the refinement of treatment options; it is important for clinicians to regularly update their practice patterns to reflect current standards. The Global Alliance to Improve Outcomes in Acne is an international group of dermatologists with an interest in acne research and education that has been meeting regularly since 2001. As a group, we have continuously evaluated the literature on acne. This supplement focuses on providing relevant clinical guidance to health care practitioners managing patients with acne, with an emphasis on areas where the evidence base may be sparse or need interpretation for daily practice.

Skin fibrotic disorders are understood to develop under the influence of some growth factors, such as transforming growth factor-beta (TGF-beta), basic fibroblast growth factor (bFGF), or connective tissue growth factor (CTGF). To establish an appropriate animal model of skin fibrosis by exogenous application of growth factors, we investigated the in vivo effects of growth factors by injecting TGF-beta, CTGF, and bFGF into the subcutaneous tissue of newborn mice. A single application of TGF-beta or bFGF resulted in the formation of transient granulated tissue that disappeared despite 7 days of consecutive injections. A single CTGF injection also caused slight granulation. However, injecting TGF-beta plus CTGF produced long-term fibrotic tissue, which persisted for at least 14 days. Also, fibrotic tissue was observed when CTGF was injected from 4 to 7 days after TGF-beta injections for the first 1-3 days. In situ hybridization analysis revealed the expression of CTGF mRNA in the fibroblasts at least in a few fibrotic conditions. These findings suggest that either CTGF mRNA or an application of exogenous CTGF protein is required for the development of persistent fibrosis. From our study, it appears that interaction of several growth factors is required for persistent fibrotic tissue formation, with TGF-beta causing the induction and CTGF needed for maintenance of skin fibrosis. The animal model on skin fibrosis by exogenous application of growth factors developed in this study may prove useful for future studies on fibrotic disorders.

BackgroundTo improve the quality of life of colorectal cancer patients, it is important to establish new screening methods for early diagnosis of colorectal cancer.Methodology/Principal FindingsWe performed serum metabolome analysis using gas-chromatography/mass-spectrometry (GC/MS). First, the accuracy of our GC/MS-based serum metabolomic analytical method was evaluated by calculating the RSD% values of serum levels of various metabolites. Second, the intra-day (morning, daytime, and night) and inter-day (among 3 days) variances of serum metabolite levels were examined. Then, serum metabolite levels were compared between colorectal cancer patients (N = 60; N = 12 for each stage from 0 to 4) and age- and sex-matched healthy volunteers (N = 60) as a training set. The metabolites whose levels displayed significant changes were subjected to multiple logistic regression analysis using the stepwise variable selection method, and a colorectal cancer prediction model was established. The prediction model was composed of 2-hydroxybutyrate, aspartic acid, kynurenine, and cystamine, and its AUC, sensitivity, specificity, and accuracy were 0.9097, 85.0%, 85.0%, and 85.0%, respectively, according to the training set data. In contrast, the sensitivity, specificity, and accuracy of CEA were 35.0%, 96.7%, and 65.8%, respectively, and those of CA19-9 were 16.7%, 100%, and 58.3%, respectively. The validity of the prediction model was confirmed using colorectal cancer patients (N = 59) and healthy volunteers (N = 63) as a validation set. At the validation set, the sensitivity, specificity, and accuracy of the prediction model were 83.1%, 81.0%, and 82.0%, respectively, and these values were almost the same as those obtained with the training set. In addition, the model displayed high sensitivity for detecting stage 0–2 colorectal cancer (82.8%).Conclusions/SignificanceOur prediction model established via GC/MS-based serum metabolomic analysis is valuable for early detection of colorectal cancer and has the potential to become a novel screening test for colorectal cancer.

Background: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer.Methods: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study.Results: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers.Conclusions: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis.Impact: It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis. Cancer Epidemiol Biomarkers Prev; 22(4); 571-9. Ó2013 AACR.