Perspectives on the revised Ghent criteria for the diagnosis of Marfan syndrome

Three international nosologies have been proposed for the diagnosis of Marfan syndrome (MFS): the Berlin nosology in 1988; the Ghent nosology in 1996 (Ghent-1); and the revised Ghent nosology in 2010 (Ghent-2). We reviewed the literature and discussed the challenges and concepts of diagnosing MFS in adults. Ghent-1 proposed more stringent clinical criteria, which led to the confirmation of MFS in only 32%-53% of patients formerly diagnosed with MFS according to the Berlin nosology. Conversely, both the Ghent-1 and Ghent-2 nosologies diagnosed MFS, and both yielded similar frequencies of MFS in persons with a causative FBN1 mutation (90% for Ghent-1 versus 92% for Ghent-2) and in persons not having a causative FBN1 mutation (15% versus 13%). Quality criteria for diagnostic methods include objectivity, reliability, and validity. However, the nosology-based diagnosis of MFS lacks a diagnostic reference standard and, hence, quality criteria such as sensitivity, specificity, or accuracy cannot be assessed. Medical utility of diagnosis implies congruency with the historical criteria of MFS, as well as with information about the etiology, pathogenesis, diagnostic triggers, prognostic triggers, and potential complications of MFS. In addition, social and psychological utilities of diagnostic criteria include acceptance by patients, patient organizations, clinicians and scientists, practicability, costs, and the reduction of anxiety. Since the utility of a diagnosis or exclusion of MFS is context-dependent, prioritization of utilities is a strategic decision in the process of nosology development. Screening tests for MFS should be used to identify persons with MFS. To confirm the diagnosis of MFS, Ghent-1 and Ghent-2 perform similarly, but Ghent-2 is easier to use. To maximize the utility of the diagnostic criteria of MFS, a fair and transparent process of nosology development is essential

Input-Driven Tissue P Automata

We introduce several variants of input-driven tissue P automata where the rules to be applied only depend on the input symbol. Both strings and multisets are considered as input objects; the strings are either read from an input tape or defined by the sequence of symbols taken in, and the multisets are given in an input cell at the beginning of a computation, enclosed in a vesicle. Additional symbols generated during a computation are stored in this vesicle, too. An input is accepted when the vesicle reaches a final cell and it is empty. The computational power of some variants of input-driven tissue P automata is illustrated by examples and compared with the power of the input-driven variants of other automata as register machines and counter automata

Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized management and prevention of cancer.Comment: 5 figs, related papers, visit lab homepage: http://www.cancer-systemsbiology.org, Seminar in Cancer Biology, 201

Mitochondrial disease associated with complex I (NADH-CoQ oxidoreductase) deficiency.

Mitochondrial diseases due to a reduced capacity for oxidative phosphorylation were first identified more than 20 years ago, and their incidence is now recognized to be quite significant. In a large proportion of cases the problem can be traced to a complex I (NADH-CoQ oxidoreductase) deficiency (Phenotype MIM #252010). Because the complex consists of 44 subunits, there are many potential targets for pathogenic mutations, both on the nuclear and mitochondrial genomes. Surprisingly, however, almost half of the complex I deficiencies are due to defects in as yet unidentified genes that encode proteins other than the structural proteins of the complex. This review attempts to summarize what we know about the molecular basis of complex I deficiencies: mutations in the known structural genes, and mutations in an increasing number of genes encoding "assembly factors", that is, proteins required for the biogenesis of a functional complex I that are not found in the final complex I. More such genes must be identified before definitive genetic counselling can be applied in all cases of affected families

Massively parallel computing on an organic molecular layer

Current computers operate at enormous speeds of ~10^13 bits/s, but their principle of sequential logic operation has remained unchanged since the 1950s. Though our brain is much slower on a per-neuron base (~10^3 firings/s), it is capable of remarkable decision-making based on the collective operations of millions of neurons at a time in ever-evolving neural circuitry. Here we use molecular switches to build an assembly where each molecule communicates-like neurons-with many neighbors simultaneously. The assembly's ability to reconfigure itself spontaneously for a new problem allows us to realize conventional computing constructs like logic gates and Voronoi decompositions, as well as to reproduce two natural phenomena: heat diffusion and the mutation of normal cells to cancer cells. This is a shift from the current static computing paradigm of serial bit-processing to a regime in which a large number of bits are processed in parallel in dynamically changing hardware.Comment: 25 pages, 6 figure

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

Colorectal Cancer Through Simulation and Experiment

Colorectal cancer has continued to generate a huge amount of research interest over several decades, forming a canonical example of tumourigenesis since its use in Fearon and Vogelstein’s linear model of genetic mutation. Over time, the field has witnessed a transition from solely experimental work to the inclusion of mathematical biology and computer-based modelling. The fusion of these disciplines has the potential to provide valuable insights into oncologic processes, but also presents the challenge of uniting many diverse perspectives. Furthermore, the cancer cell phenotype defined by the ‘Hallmarks of Cancer’ has been extended in recent times and provides an excellent basis for future research. We present a timely summary of the literature relating to colorectal cancer, addressing the traditional experimental findings, summarising the key mathematical and computational approaches, and emphasising the role of the Hallmarks in current and future developments. We conclude with a discussion of interdisciplinary work, outlining areas of experimental interest which would benefit from the insight that mathematical and computational modelling can provide