Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.Comment: 25 page

Advances in analytical tools and current statistical methods used in ultra-high-performance liquid chromatography-mass spectrometry of glycero-, glycerophospho- and sphingolipids

The review concentrates on the properties of analytical and statistical ultrahigh-performance liquid chromatographic (UHPLC) - mass spectrometric (MS) methods suitable for glycero-, glycerophospho- and sphingolipids in lipidomics published between the years 2017 2019. Trends and fluctuations of conventional and nano-UHPLC methods with MS and tandem MS detection were observed in context of analysis conditions and tools used for data-analysis. Whereas general workflow characteristics are agreed upon, more details related to the chromatographic methodology (i.e. stationary and mobile phase conditions) need evidently agreements. Lipid quantitation relies upon isotope-labelled standards in targeted analyses and fully standardless algorithm-based untargeted analyses. Furthermore, a wide spectrum of setups have shown potential for the elucidation of complex and large datasets by minimizing the risks of systematic misinterpretation like false positives. This kind of evaluation was shown to have increased importance and usage for cross-validation and data-analysis. (C) 2020 Elsevier B.V. All rights reserved.Peer reviewe

Lab-on-a-Chip Fabrication and Application

The necessity of on-site, fast, sensitive, and cheap complex laboratory analysis, associated with the advances in the microfabrication technologies and the microfluidics, made it possible for the creation of the innovative device lab-on-a-chip (LOC), by which we would be able to scale a single or multiple laboratory processes down to a chip format. The present book is dedicated to the LOC devices from two points of view: LOC fabrication and LOC application

Data mining applied to the Varicocele condition

O sistema de saúde guarda cada vez mais informação dos seus utentes o que dificulta ou até impossibilita a descoberta de novos conhecimentos só com as técnicas usualmente utilizadas, i.e., as tradicionais técnicas estatísticas. De facto, os investigadores clínicos têm sentido uma crescente necessidade em extrair novos conhecimentos para continuadamente contribuir para o melhoramento dos serviços de saúde prestados. Essa necessidade tem vindo a ser colmatada com a aplicação de um processo, chamado “data mining”, que auxilia, através da aplicação de diversas técnicas (i.e., classificação, clustering, associação, etc.), a descoberta de padrões de dados vistos como interessantes, mas ocultados com as tradicionais técnicas estatísticas. A área da infertilidade masculina já começou a aplicar o data mining, por exemplo, através da aplicação da técnica de classificação para prever o sucesso de uma técnica de Procriação Medicamente Assistida. Contudo, o varicocelo - um síndrome anatómico de varizes escrotais caracterizado pela dilatação das veias que drenam o sangue da região dos testículos que em certos casos dá origem à infertilidade - não foi ainda explorado com uma técnica de data mining. A sua prevalência atinge 40% dos homens tratados por infertilidade, sendo que a infertilidade masculina abrange 50% das causas da infertilidade de um casal. A correção do varicocelo pode ser alcançada com um tratamento radiológico chamado embolização, que tem por objetivo desvitalizar as veias dilatadas através da introdução de substâncias terapêuticas na circulação sanguínea. Neste contexto, este trabalho teve os seguintes principais objetivos: i) averiguar o sucesso da correção do varicocelo com a técnica da embolização através da identificação de algum melhoramento na média dos valores dos parâmetros seminais ou das categorias seminais com recurso a técnicas estatísticas inferenciais (i.e. ANOVA e Chi-quadrado); ii) predizer o sucesso da embolização com técnicas de classificação através da aplicação do decision tree do RapidMiner e do algoritmo W-J48; iii) identificar padrões que caracterizam os pacientes embolizados com a técnica de clustering através do algoritmo K-Means e eleger as relações de atributos que ocorrem mais frequentemente através da técnica de associação com o algoritmo FP-Growth. Este processo de análise de dados seguiu a metodologia Cross-Industry Standard Process for Data mining (CRISP-DM) aplicando-a à análise de uma amostra de 293 homens inférteis descritos com 64 atributos que foram submetidos à embolização no Centro Hospitalar e Universitário de Coimbra (CHUC) entre Janeiro de 2007 e Abril de 2016. Os resultados obtidos indicam que a embolização melhora significativamente a média das concentrações de espermatozoides até 12 meses e de suas morfologias até 6 meses depois da embolização (ANOVA p<0.05) o que permite fundamentar o interesse em prever o sucesso desta técnica terapêutica. Sua previsão computarizada com a árvore de decisão do RapidMiner permitiu prever com uma Accuracy e F-measure de 70.59% e uma AUC de 0.750 que a probabilidade condicional de engravidar tendo um homem com uma severidade baixa ou média do varicocelo e uma parceira entre os 24 e 33 anos inclusive é de 70.83%. Também se viu que a frequência relativa, de pacientes com uma concentração de espermatozoides normal 3 meses depois da embolização e uma motilidade progressiva normal destes antes do tratamento, é mais alta em grupos de pacientes que raramente trabalham em ambientes tóxicos. Estes resultados permitem contribuir para as investigações em curso no domínio da infertilidade, assim como nidentificação de medidas que permitem um maior auxílio na descoberta do conhecimento. Nomeadamente, vimos que a aplicação conjunta dos algoritmos de data mining com as técnicas estatísticas inferenciais, assim como a aplicação de diversas técnicas de data mining (i.e., classificação, clustering e associação), potencia a descoberta do conhecimento em dados clínicos

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Recent Developments and Challenges in Chromatographic Lipidomics

Lipidomics is a quickly growing trend in metabolomics research: not only seen as passive cell membrane building blocks, lipids contribute actively to cell signaling and identification, thus seen as potential biomarkers (e.g. for early stage cancer diagnostics). The literature part includes a review of 63 articles on UHPLC/MS-methods in the time frame of 2017-05/2019. The following literature is focused especially on glycerophospholipids (GPs). In addition, an overview to basic glycerolipids (GLs) and sphingolipids (SPs) is established, which evidently affects the emphasis and narration of lipid class representations in this review. Chromatographic methods in lipidomics are used to achieve either very selective or all-encompassing analyses for lipid classes. Since HPLC/MS is an insufficient method for fully encompassing low-abundance lipids, UHPLC/MS was mostly used for metabolic profiling where its large analyte range due to high sensitivity, separation efficiency and resolution excels in performance compared to other methods. Imaging techniques have further diverted towards DIMS and other novel non-chromatographic methods, e.g. Raman techniques with single cell resolution. The field of mass-spectral lipidomics is divided between studies using isotope-labeled standards or fully standardless algorithm-based analyses, furthermore, machine learning and statistical analysis has increased. The experimental part focused on LC-IMS-MS and plasma-based in-house database method development for targeted analysis of ascites. Method development included optimization of the chromatography, adduct species selection and data-independent/-dependent fragmentation. Totally, 130 potential species from the LIPID MAPS database were used for the identification at the minimum score of 79% for identification in the Qualitative Workflows with retention times (RTs) and Mass Profiler-program with collision cross-sections (CCSs). Plasma sample analyses resulted in the documentation of 70 RTs and 36 CCS values. Two lipid extraction methods (Folch and BUME) with pre-sampling surrogates and post-sampling internal standards were compared with each other. The process resulted in confirming the BUME method in lipidomics to be superior in ecology-, workload-, health- and extraction-related properties. The lipidome of ascites has rarely been studied due to its availability only in diseased patients. Also, limiting factors for these studies are the logistics to realise such a representative analysis