Studies in steric hindrance with special reference to the aromatic iodo-chlorides

An examination of the formation of the aromatic iodo- chlorides has been carried out. Difficulty was experienced in the isolation of the dichlorides of certain diortho -substituted iodo -compounds. Further, quantitative experiments have shown that this difficulty is due, in some cases, to the instability of the dichloride and to the appearance of side reactions. Therefore the quantitative results cannot be taken as being fundamental until experiments have been carried out taking all these factors into consideration. The quantitative results have shown, however, that no ¿eneral hindrance to the reaction has been encountered. From scale diagrams of diortho-substituted iodobenzene molecules this is to be expected. Only in two cases - with the compounds 4.6- dibromo -2- nitro -iodobenzene and 4- iodo -3 :5- dinitrotoluene - has an appreciable retardation of the reaction occurred. It is not certain whether this is due solely to steric causes. No difference in reactivity of substituted a- and i3- iodonaphthalenes has been observed. The dichlorides of ¡3- iodonaphthalenes have proved to be very unstable. It was shown, in the case of 1;6- dibromo- t3- iodonaphthalene, that decomposition is accompanied by nuclear chlorination. One new iodine derivative in the benzene series and two in the naphthalene series have been isolated. Several hitherto unknown dichlorides have been described. In the course of a brief examination it has been shown that, whereas a- substituted aceto -¡3- naphthalides are easily hydrolysed using alcoholid hydrochloric acid, 3- substituted aceto -a- naphthalides are resistant to this reagent. Finally, condensation experiments have been carried out with certain substituted thiobenzoic acids. The method adopted for the preparation of the unknown o- chloro- and o- bromo- thiobenzoic acids is suggested as a general method for the preparation of mono - substituted" thio- acids. More vigorous methods were found necessary for the preparation of the new 2:4:6 - tribromo- thiobenzoic acid. In the course of this part of the research two new substituted dibenzoyl disulphides and several new substituted benzanilides were isolated. New lines for future research have been suggested

Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data

BACKGROUND: A critical step in processing oligonucleotide microarray data is combining the information in multiple probes to produce a single number that best captures the expression level of a RNA transcript. Several systematic studies comparing multiple methods for array processing have used tightly controlled calibration data sets as the basis for comparison. Here we compare performances for seven processing methods using two data sets originally collected for disease profiling studies. An emphasis is placed on understanding sensitivity for detecting differentially expressed genes in terms of two key statistical determinants: test statistic variability for non-differentially expressed genes, and test statistic size for truly differentially expressed genes. RESULTS: In the two data sets considered here, up to seven-fold variation across the processing methods was found in the number of genes detected at a given false discovery rate (FDR). The best performing methods called up to 90% of the same genes differentially expressed, had less variable test statistics under randomization, and had a greater number of large test statistics in the experimental data. Poor performance of one method was directly tied to a tendency to produce highly variable test statistic values under randomization. Based on an overall measure of performance, two of the seven methods (Dchip and a trimmed mean approach) are superior in the two data sets considered here. Two other methods (MAS5 and GCRMA-EB) are inferior, while results for the other three methods are mixed. CONCLUSIONS: Choice of processing method has a major impact on differential expression analysis of microarray data. Previously reported performance analyses using tightly controlled calibration data sets are not highly consistent with results reported here using data from human tissue samples. Performance of array processing methods in disease profiling and other realistic biological studies should be given greater consideration when comparing Affymetrix processing methods

Visual Analytics for Epidemiologists: Understanding the Interactions Between Age, Time, and Disease with Multi-Panel Graphs

Visual analytics, a technique aiding data analysis and decision making, is a novel tool that allows for a better understanding of the context of complex systems. Public health professionals can greatly benefit from this technique since context is integral in disease monitoring and biosurveillance. We propose a graphical tool that can reveal the distribution of an outcome by time and age simultaneously.We introduce and demonstrate multi-panel (MP) graphs applied in four different settings: U.S. national influenza-associated and salmonellosis-associated hospitalizations among the older adult population (≥65 years old), 1991-2004; confirmed salmonellosis cases reported to the Massachusetts Department of Public Health for the general population, 2004-2005; and asthma-associated hospital visits for children aged 0-18 at Milwaukee Children's Hospital of Wisconsin, 1997-2006. We illustrate trends and anomalies that otherwise would be obscured by traditional visualization techniques such as case pyramids and time-series plots.MP graphs can weave together two vital dynamics--temporality and demographics--that play important roles in the distribution and spread of diseases, making these graphs a powerful tool for public health and disease biosurveillance efforts

Application of Biomarkers in Cancer Risk Management: Evaluation from Stochastic Clonal Evolutionary and Dynamic System Optimization Points of View

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic “biomarkers” have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time

A European research agenda for somatic symptom disorders, bodily distress disorders, and functional disorders: Results of an estimate-talk-estimate delphi expert study

Background: Somatic Symptom Disorders (SSD), Bodily Distress Disorders (BDD) and functional disorders (FD) are associated with high medical and societal costs and pose a substantial challenge to the population and health policy of Europe. To meet this challenge, a specific research agenda is needed as one of the cornerstones of sustainable mental health research and health policy for SSD, BDD, and FD in Europe. Aim: To identify the main challenges and research priorities concerning SSD, BDD, and FD from a European perspective. Methods: Delphi study conducted from July 2016 until October 2017 in 3 rounds with 3 workshop meetings and 3 online surveys, involving 75 experts and 21 European countries. EURONET-SOMA and the European Association of Psychosomatic Medicine (EAPM) hosted the meetings. Results: Eight research priorities were identified: (1) Assessment of diagnostic profiles relevant to course and treatment outcome. (2) Development and evaluation of new, effective interventions. (3) Validation studies on questionnaires or semi-structured interviews that assess chronic medical conditions in this context. (4) Research into patients preferences for diagnosis and treatment. (5) Development of new methodologic designs to identify and explore mediators and moderators of clinical course and treatment outcomes (6). Translational research exploring how psychological and somatic symptoms develop from somatic conditions and biological and behavioral pathogenic factors. (7) Development of new, effective interventions to personalize treatment. (8) Implementation studies of treatment interventions in different settings, such as primary care, occupational care, general hospital and specialty mental health settings. The general public and policymakers will benefit from the development of new, effective, personalized interventions for SSD, BDD, and FD, that will be enhanced by translational research, as well as from the outcomes of research into patient involvement, GP-patient communication, consultation-liaison models and implementation. Conclusion: Funding for this research agenda, targeting these challenges in coordinated research networks such as EURONET-SOMA and EAPM, and systematically allocating resources by policymakers to this critical area in mental and physical well-being is urgently needed to improve efficacy and impact for diagnosis and treatment of SSD, BDD, and FD across Europe

Induction of Immune Mediators in Glioma and Prostate Cancer Cells by Non-Lethal Photodynamic Therapy

BACKGROUND: Photodynamic therapy (PDT) uses the combination of photosensitizing drugs and harmless light to cause selective damage to tumor cells. PDT is therefore an option for focal therapy of localized disease or for otherwise unresectable tumors. In addition, there is increasing evidence that PDT can induce systemic anti-tumor immunity, supporting control of tumor cells, which were not eliminated by the primary treatment. However, the effect of non-lethal PDT on the behavior and malignant potential of tumor cells surviving PDT is molecularly not well defined. METHODOLOGY/PRINCIPAL FINDINGS: Here we have evaluated changes in the transcriptome of human glioblastoma (U87, U373) and human (PC-3, DU145) and murine prostate cancer cells (TRAMP-C1, TRAMP-C2) after non-lethal PDT in vitro and in vivo using oligonucleotide microarray analyses. We found that the overall response was similar between the different cell lines and photosensitizers both in vitro and in vivo. The most prominently upregulated genes encoded proteins that belong to pathways activated by cellular stress or are involved in cell cycle arrest. This response was similar to the rescue response of tumor cells following high-dose PDT. In contrast, tumor cells dealing with non-lethal PDT were found to significantly upregulate a number of immune genes, which included the chemokine genes CXCL2, CXCL3 and IL8/CXCL8 as well as the genes for IL6 and its receptor IL6R, which can stimulate proinflammatory reactions, while IL6 and IL6R can also enhance tumor growth. CONCLUSIONS: Our results indicate that PDT can support anti-tumor immune responses and is, therefore, a rational therapy even if tumor cells cannot be completely eliminated by primary phototoxic mechanisms alone. However, non-lethal PDT can also stimulate tumor growth-promoting autocrine loops, as seen by the upregulation of IL6 and its receptor. Thus the efficacy of PDT to treat tumors may be improved by controlling unwanted and potentially deleterious growth-stimulatory pathways

PTRF/Cavin-1 and MIF Proteins Are Identified as Non-Small Cell Lung Cancer Biomarkers by Label-Free Proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the “omics” era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice