Douglas B. Kell

Bio: Douglas B. Kell is an academic researcher from University of Liverpool. The author has contributed to research in topics: Dielectric & Systems biology. The author has an hindex of 111, co-authored 634 publications receiving 50335 citations. Previous affiliations of Douglas B. Kell include Max Planck Society & University of Wales.

The Occurrence of Hyperactivated Platelets and Fibrinaloid Microclots in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Abstract: We have previously demonstrated that platelet-poor plasma (PPP) obtained from patients with Long COVID/Post-Acute Sequelae of COVID-19 (PASC) is characterized by a hypercoagulable state and contains hyperactivated platelets and considerable numbers of already-formed amyloid fibrin(ogen) or fibrinaloid microclots. Due to the substantial overlap in symptoms and etiology between Long COVID/PASC and ME/CFS, we investigated whether coagulopathies reflected in Long COVID/PASC—hypercoagulability, platelet hyperactivation, and fibrinaloid microclot formation—were present in individuals with ME/CFS and gender- and age-matched healthy controls. ME/CFS samples showed significant hypercoagulability as judged by thromboelastography of both whole blood and platelet-poor plasma. The area of plasma images containing fibrinaloid microclots was commonly more than 10-fold greater in untreated PPP from individuals with ME/CFS than in that of healthy controls. A similar difference was found when the plasma samples were treated with thrombin. Using fluorescently labelled PAC-1, which recognizes glycoprotein IIb/IIIa, and CD62P, which binds P-selectin, we observed hyperactivation of platelets in ME/CFS hematocrit samples. Using a quantitative scoring system, the ME/CFS platelets were found to have a mean spreading score of 2.72 ± 1.24 vs. 1.00 (activation with pseudopodia formation) for healthy controls. We conclude that ME/CFS is accompanied by substantial and measurable changes in coagulability, platelet hyperactivation, and fibrinaloid microclot formation. However, the fibrinaloid microclot load was not as great as was previously noted in Long COVID/PASC. Fibrinaloid microclots, in particular, may contribute to many ME/CFS symptoms, such as fatigue, seen in patients with ME/CFS, via the (temporary) blockage of microcapillaries and hence ischemia. Furthermore, fibrinaloid microclots might damage the endothelium. The discovery of these biomarkers represents an important development in ME/CFS research. It also points to possible uses for treatment strategies using known drugs and/or nutraceuticals that target systemic vascular pathology and endothelial inflammation.

Flow Cytometric Analysis, Using Rhodamine 123, of Micrococcus luteus at Low Growth Rate in Chemostat Culture

Abstract: In microbiology it is often necessary to determine the number of viable cells in a sample or culture of interest. This is usually achieved by plating out the sample (diluted as required) on to an agar plate (Postgate 1969; Hattori 1988). There are several problems associated with this technique, the greatest of which is the length of time required to obtain the results. For some slowly growing organisms (e.g. Mycobacteria) it may take in excess of a week to determine how many cells were “viable” in the original sample, and even when the sample contains fast-growing organisms and the plates are incubated under optimal growth conditions a minimum of overnight growth is usually required before the resulting colonies can be counted. For some clinical specimens even an overnight incubation may be too long to be of use and consequently many alternatives to plate counts have been proposed in order to decrease the time required to determine numbers of viable cells (Harris and Kell 1985).

Quantification of microbial productivity via multi-angle light scattering and supervised learning.

Abstract: This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of this approach for estimation of yeast cell counts over a wide range of values (10(5)-10(9) cells mL-1), although it was less successful in predicting cell viability in such suspensions.

Improving Data Fitting of a Signal Transduction Model by Global Sensitivity Analysis

Abstract: Based on a simplified model of the (TNF-alpha mediated) IkappaBalpha-NF-kappaB signal transduction pathway, global sensitivity analysis has been performed to identify those parameters that exert significant control on the system outputs. The permutation operation in Morris method is modified to work for log-uniform sampling parameters. The identified sensitive parameters are then estimated using multivariable search such that the output of the model matches experimental data representing the nuclear concentration of NF-kappaB. Such parameter tuning leads to much better agreement between the model and the experimental time series relative to those previously published. This shows the importance of global sensitivity analysis in Systems Biology models.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products

Abstract: We have developed a simple and highly efficient method to disrupt chromosomal genes in Escherichia coli in which PCR primers provide the homology to the targeted gene(s). In this procedure, recombination requires the phage lambda Red recombinase, which is synthesized under the control of an inducible promoter on an easily curable, low copy number plasmid. To demonstrate the utility of this approach, we generated PCR products by using primers with 36- to 50-nt extensions that are homologous to regions adjacent to the gene to be inactivated and template plasmids carrying antibiotic resistance genes that are flanked by FRT (FLP recognition target) sites. By using the respective PCR products, we made 13 different disruptions of chromosomal genes. Mutants of the arcB, cyaA, lacZYA, ompR-envZ, phnR, pstB, pstCA, pstS, pstSCAB-phoU, recA, and torSTRCAD genes or operons were isolated as antibiotic-resistant colonies after the introduction into bacteria carrying a Red expression plasmid of synthetic (PCR-generated) DNA. The resistance genes were then eliminated by using a helper plasmid encoding the FLP recombinase which is also easily curable. This procedure should be widely useful, especially in genome analysis of E. coli and other bacteria because the procedure can be done in wild-type cells.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

Abstract: The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.