Biomarker discovery study design consistent with the receiver-operator characteristic.

Striving towards excellence in research on biomarkers.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic and debilitating pain disorder of the bladder and urinary tract with poorly understood etiology. A definitive diagnosis of IC/BPS can be challenging because many symptoms are shared with other urological disorders. An analysis of urine presents an attractive and non-invasive resource for monitoring and diagnosing IC/BPS. The antiproliferative factor (APF) peptide has been previously identified in the urine of IC/BPS patients and is a proposed biomarker for the disorder. Nevertheless, other small urinary peptides have remained uninvestigated in IC/BPS primarily because protein biomarker discovery efforts employ protocols that remove small endogenous peptides. The purpose of this study is to investigate the profile of endogenous peptides in IC/BPS patient urine, with the goal of identifying putative peptide biomarkers. Here, a non-targeted peptidomics analysis of urine samples collected from IC/BPS patients were compared to urine samples from asymptomatic controls. Our results show a general increase in the abundance of urinary peptides in IC/BPS patients, which is consistent with an increase in inflammation and protease activity characteristic of this disorder. In total, 71 peptides generated from 39 different proteins were found to be significantly altered in IC/BPS. Five urinary peptides with high variable importance in projection (VIP) coefficients were found to reliably differentiate IC/BPS from healthy controls by receiver operating characteristic (ROC) analysis. In parallel, we also developed a targeted multiple reaction monitoring method to quantify the relative abundance of the APF peptide from patient urine samples. Although the APF peptide was found in moderately higher abundance in IC/BPS relative to control urine, our results show that the APF peptide was inconsistently present in urine, suggesting that its utility as a sole biomarker of IC/BPS may be limited. Overall, our results revealed new insights into the profile of urinary peptides in IC/BPS that will aid in future biomarker discovery and validation efforts.

Background : A group of inorganic non-metal biomaterials, that are commonly used in clinical medicine to replace or repair tissues, can be classified as a bioceramics. This group includes bioactive glasses, glass-ceramics, hydroxy-apatite and some other calcium phosphates. In addition, some bio-inert engineering ceramics materials have become increasingly utilised, aluminum oxide, zirconium oxide and their composites being the most popular. With the developement of yttria stabilized tetragonal zirconium oxide ceramics (Y-TZP) medical community received a high strength biomaterial that is currently a material of choice for the manufacturing of medical devices. Y-TZP ceramics is becoming also increasingly used in dental medicine, where frameworks are manufactured by the use of computer-assisted technology. Conclusions : The article describes the basic properties of zirconia oxide ceramics important for the use in clinical medicine; high strength and fracture toughness, biocompatibility and negligible radiation. The ageing issue of this particular material, which is attributable to the thermo-dynamical instability of tetragonal zirconium oxide in hydrothermal conditions, is also discussed. When exposed to an aqueous environment over long periods of time, the surface of the Y-TZP ceramic will start transforming spontaneously into the monoclinic structure. The mechanism leading to the t-m transformation is temperature-dependent and is accompanied by extensive micro-cracking, which ultimately leads to strength degradation. The degradation might influence the clinical success rate of medical devices and therefore Y-TZP femoral heads are no longer made of pure zirconium oxide. Composites of zirconium and aluminium oxides are used instead, that are currently the strongest ceramic materials used in clinical medicine. In this work the clinical application of zirconia oxide ceramics in dental medicine is also presented. Conventional porcelain fused to metal technique is successfully replaced with Y-TZP ceramics in some clinical situations that are described in detail. It is important that computer design of the zirconia frameworks shortens and simplifies laboratory procedures and contributes to a precise final product.

Introduction An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment. Areas covered This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer. Expert opinion Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

The evolution of high complexity companion testing for targeted and immuno-oncology.

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.

Simple SummaryThis contribution allows for the computation of exact p-values and for conducting accurate statistical hypothesis tests of ROC AUC-values. As a result, the development of diagnostic tests is facilitated. This work is illustrated via simulated data and through the development of proteomic blood biomarkers for the early detection of cancer.The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus the exactness in its probability distribution is crucial toward accurate statistical inference. We show the exact probability distribution of the ROC AUC-value, hence exact critical values and p-values are readily obtained. Because the exact calculations are computationally intense, we demonstrate a method of geometric interpolation, which is exact in a special case but generally an approximation, vastly increasing computational speeds. The method is illustrated through open access data, demonstrating superiority of 26 composite biomarkers relative to a predicate device. Especially under correction for testing of multiple hypotheses, traditional asymptotic approximations are encumbered by considerable imprecision, adversely affecting IVD device development. The ability to obtain exact p-values will allow more efficient IVD device development.

The use of silver nanoparticles has become more widespread in our society. While many believe that silver can be extremely useful in clinical medicine, firm evidence is still lacking. Thus, we present here a review of their current use in clinical medicine.

The recombinant human erythropoietins and allied proteins (epoetin alfa, attempted copies and biosimilar variants of epoetin alfa, epoetin beta, epoetin delta, epoetin zeta, epoetin theta, epoetin omega, darbepoetin alfa, and methoxy-polyethylene glycol-epoetin beta) are among the most successful and earliest examples of biotechnologically manufactured products to be used in clinical medicine. This article charts a brief history of their use in clinical medicine, mainly dealing with chronic kidney disease, paying special attention to how these agents were introduced into clinical medicine and what has happened subsequently; in 2009, there were several developments that could be regarded as a "perfect storm" in terms of the long-term use of these compounds in chronic kidney disease and oncology and, likely, elsewhere. We are now very much at a "crossroads," where mature reflection is required, because with the latest trials and meta-analyses, these therapies seem not only expensive but also very much a clinical tradeoff (increased risk of adverse effects versus a small gain in fatigue scores). How we arrived at this crossroads is a useful illustration of how easy it is, without properly designed randomized, controlled trials, to assume that clinical benefit must follow therapeutic interventions.

A major area of research is biomarker discovery using gene expression data. Such data is huge and often needs to be classified into classes or clustered, using different machine learning techniques, for further analysis. An important preprocessing step is feature selection (FS) and different such methods have been devised. However, applying different FS techniques to the same dataset do not always produce the same results. In this work, the robustness of FS methods will be looked into. Robustness is defined here as the stability of a given gene pool with respect to the data and the FS method used. Our approach is to investigate the resulting feature subset obtained when running diverse FS methods on different gene expression datasets. As a first step, 10 FS methods were executed using 2 different datasets. Based on the results obtained, 2 of these methods were further investigated using 10 different datasets. The effects of selecting an increasing number of features on the percentage similarity inter-methods were also studied. Our results show that the studied methods exhibit a high amount of variability in the resulting feature subset. The selected feature subsets differed both inter-methods and intra-methods for different datasets. The reason behind this is not clear and possible objective assessment on the ideal (best) subset should be further investigated.

In omics studies aimed at the early detection and diagnosis of cancer, bioinformatics tools play a significant role when analyzing high dimensional, complex datasets, as well as when identifying a small set of biomarkers. However, in many cases, there are ambiguities in the robustness and the consistency of the discovered biomarker sets, since the feature selection methods often lead to irreproducible results. To address this, both the stability and the classification power of several chemometrics-based feature selection algorithms were evaluated using the Monte Carlo sampling technique, aiming at finding the most suitable feature selection methods for early cancer detection and biomarker discovery. To this end, two data sets were analyzed, which comprised of MALDI-TOF-MS and LC/TOF-MS spectra measured on serum samples in order to diagnose ovarian cancer. Using these datasets, the stability and the classification power of multiple feature subsets found by different feature selection methods were quantified by varying either the number of selected features, or the number of samples in the training set, with special emphasis placed on the property of stability. The results show that high consistency does not necessarily guarantee high predictive power. In addition, differences in the stability, as well as agreement in feature lists between several feature selection methods, depend on several factors, such as the number of available samples, feature sizes, quality of the information in the dataset, etc. Among the tested methods, only the variable importance in projection (VIP)-based method shows complementary properties, providing both highly consistent and accurate subsets of features. In addition, successive projection analysis (SPA) was excellent with regards to maintaining high stability over a wide range of experimental conditions. The stability of several feature selection methods is highly variable, stressing the importance of making the proper choice among feature selection methods. Therefore, rather than evaluating the selected features using only classification accuracy, stability measurements should be examined as well to improve the reliability of biomarker discovery.

BOOK REVIEWS Platelets: Pathophysiology and Antiplatelet Drug Therapy. By Harvey J. Weiss. New York: Alan R. Liss, 1982. Pp. 165. $22.00. Dr. Harvey J. Weiss is professor of medicine, College of Physicians and Surgeons of Columbia University, and director of the Division of HematologyOncology at St. Luke's-Roosevelt Hospital Center in New York. He has a longstanding interest in many aspects ofplatelet function and in the inhibitory effects of the so-called antiplatelet drugs. In collaboration with a number of investigators , he has made significant contributions to our present knowledge of these subjects. This is die latest in a number of useful reviews of this nature diat Dr. Weiss has written. It provides a detailed overview up to 1981, although Dr. Weiss was able to add some references to articles published in 1982. It is appropriate that this book should be reviewed in Perspectives in Biology and Mediane since it itself provides a perspective. The book is divided into four chapters, the first two, concerning platelet mechanisms and the biological properties of platelets, serving to set the stage for chapters 3 and 4, in which the pharmacological aspects of "antiplatelet drugs" are discussed and their use in clinical medicine is described widi emphasis on the large-scale clinical trials in patients widi cerebrovascular disease and ischemic heart disease. An indication of the comprehensive review provided by this book is die reference list which contains well over 800 citations. A useful index is also included. In general, Dr. Weiss has made appropriate choices of the topics that are necessary to provide an adequate background for the understanding of the effects of"antiplatelet drugs" and their uses. Of necessity, the discussion of each topic is brief, but in most cases it is focused on die key information. Although the history of the development of our understanding of subjects such as ADPinduced aggregation or prostaglandins is of interest, some of the early theories that are no longer accepted might have been omitted to permit more detailed discussion ofsubjects ofcurrent interest, such as the role offibrinogen in aggregation induced by all agents, die reversible nature ofthe complex of membrane glycoproteins that form die fibrinogen receptor, die relationship of this reversibility to platelet deaggregation, the possible role of thrombospondin in platelet aggregation, the synergistic effects among aggregating agents, and the activation of the phosphoinositol-phosphatidic acid cycle in response to release-inducing agents. However, in a relatively short chapter ranging from platelet production through rheology, structure, activation, function, metabolism, to regulation and role in coagulation, diere are bound to be a few Permission to reprint a book review printed in diis section may be obtained only from the author. 314 Book Reviews omissions, and, of course, we tend to notice when topics in which we are most interested are not mentioned. Chapter 2 gives a clear summary of the role of platelets in hemostasis and the platelet abnormalities that result in impairment of hemostasis. Thrombosis and atherogenesis are treated more briefly, with litde consideration of the causes of vessel wall injury that may initiate thrombosis and atherosclerosis, or of die eventual dissolution or resolution of thrombi. The roles of platelets in maintaining vascular integrity, and in inflammation, immunological reactions, and malignancy are quite rightly identified as important biological properties of platelets. The inclusion of platelet-activating factor (PAF) in the section on immunological reactions, rather than as a platelet aggregating agent, is appropriate since it seems unlikely that PAF has much involvement in the aggregation of human platelets. Chapter 3 on the pharmacologic aspects of antiplatelet drugs concentrates on the drugs that are currently in use or have the potential to be used in clinical medicine. It is organized into sections on drugs that inhibit the formation of thromboxane A2 by platelets and PGI2 formation by the vessel wall, agents that raise cyclic AMP in platelets, drugs that act on die platelet membrane, anticoagulants , and a number ofdrugs that act in less well-defined ways. Drugs diat inhibit cyclo-oxygenase receive the most attention since they have been most extensively studied in all respects, including clinical trials. The rationale for the development of thromboxane synthetase inhibitors is not discussed, although, theoretically, their ability...

A multi-dimensional non-uniform corrosion model for bioabsorbable metallic vascular stents

INTRODUCTION A validated and effective gene expression-based, biomarker discovery process can be an incredibly valuable and often necessary tool in drug discovery, development, and diagnostic research. However, to successfully implement a biomarker signature discovery program and mine the most value out of such a program, one needs to have access to a broad range of expertise, both biological and statistical, as well as sophisticated tools for performing the laboratory and data analysis processes that enable research and commercial applications. Access to these skills and the tools necessary to support biomarker or signature discovery efforts may come from internally funded and constructed resources or through exterior collaborations, or in the case of the Signature DiscoveryTM Program developed by Althea Technologies (San Diego, CA, USA) and Expression Analysis (Durham, NC, USA), outsourced to a sole-source development provider. Whichever direction best suits your development and validation needs, several critical steps should be closely adhered to, from both biological and statistical perspectives. Our goal here is to provide an overview and some insights that identify these critical paths along your discovery activities.

The amount of spam is increasing rapidly while the popularity of emails is increasing. This situation has led to the need to filter spam emails. To date, many knowledge-based, learning-based, and clustering-based methods have been developed for filtering spam emails. In this study, machine-learning-based spam detection was targeted, and C4.5, ID3, RndTree, C-Support Vector Classification (C-SVC), and Naïve Bayes algorithms were used for email spam detection. In addition, feature selection and data transformation methods were used to increase spam detection success. Experiments were performed on the UC Irvine Machine Learning Repository (UCI) spambase dataset, and the results were compared for accuracy, Receiver Operating Characteristic (ROC) analysis, and classification speed. According to the accuracy comparison, the C-SVC algorithm gave the highest accuracy with 93.13%, followed by the RndTree algorithm. According to the ROC analysis, the RndTree algorithm gave the best Area Under Curve (AUC) value of 0.999, while the C4.5 algorithm gave the second-best result. The most successful methods in terms of classification speed are Naïve Bayes and RndTree algorithms. In the experiments, it was seen that feature selection and data transformation methods increased spam detection success. The binary transformation that increased the classification success the most and the feature selection method was forward selection.