Clinical Decision-Making: Developing a 4 C Model Using Graph Theoretic Approach

The purpose of this paper is to propose a graph-theoretic mathematical model to measure how conducive the environment of a hospital is for decision-making. We propose a 4-C model, developed from four interacting factors: confidence, complexity, capability, and customer. In this graph-theoretic model, abstract information regarding the system is represented by the directed edges of a graph (or digraph), which together depict how one factor affects another. The digraph yields a matrix model useful for computer processing. The net effect of different factors and their interdependencies on the hospital's decision-making environment is quantified and a single numerical index is generated. This paper categorizes all the major factors that influence clinical decision-making and attempts to provide a tool to study and measure their interactions with each other. Each factor and each interaction among factors are to be quantified by healthcare experts according to their best judgment of the magnitude of its effect in a local hospital environment.A hospital case study is used to demonstrate how the 4-C model works. The graph-theoretic approach allows for the inclusion of new factors and generation of alternative environments by a combination of both qualitative and quantitative modeling. The 4-C model can be used to create both a database and a simple numerical scale that help a hospital set customized guidelines, ranging from patient admittance procedures to diagnostic and treatment processes, according to its specific situation. Implementing this methodology systematically can allow a hospital to identify factors that will lead to improved decision-making as well as identifying operational factors that present roadblocks

Colloidal Nanocrystals Embedded in Macrocrystals: Robustness, Photostability, and Color Purity

Cataloged from PDF version of article.The incorporation of colloidal quantum dots (QDs) into ionic crystals of various salts (NaCl, KCl, KBr, etc.) is demonstrated. The resulting mixed crystals of various shapes and beautiful colors preserve the strong luminescence of the incorporated QDs. Moreover, the ionic salts appear to be very tight matrices, ensuring the protection of the QDs from the environment and as a result providing them with extraordinary high photo- and chemical stability. A prototype of a white light-emitting diode (WLED) with a color conversion layer consisting of this kind of mixed crystals is demonstrated. These materials may also find applications in nonlinear optics and as luminescence standards

Steel corrosion in reinforced alkali-activated materials

The development of alkali-activated materials (AAMs) as an alternative to Portland cement (PC) has seen significant progress in the past decades. However, there still remains significant uncertainty regarding their long term performance when used in steel-reinforced structures. The durability of AAMs in such applications depends strongly on the corrosion behaviour of the embedded steel reinforcement, and the experimental data in the literature are limited and in some cases inconsistent. This letter elucidates the role of the chemistry of AAMs on the mechanisms governing passivation and chloride-induced corrosion of the steel reinforcement, to bring a better understanding of the durability of AAM structures exposed to chloride. The corrosion of the steel reinforcement in AAMs differs significantly from observations in PC; the onset of pitting (or the chloride ‘threshold’ value) depends strongly on the alkalinity, and the redox environment, of these binders. Classifications or standards used to assess the severity of steel corrosion in PC appear not to be directly applicable to AAMs due to important differences in pore solution chemistry and phase assemblage

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Plasma total cell-free DNA (cfDNA) is a surrogate biomarker for tumour burden and a prognostic biomarker for survival in metastatic melanoma patients

Introduction Tumour burden is a prognostic biomarker in metastatic melanoma. However, tumour burden is difficult to measure and there are currently no reliable surrogate biomarkers to easily and reliably determine it. The aim of this study was to assess the potential of plasma total cell free DNA as biomarker of tumour burden and prognosis in metastatic melanoma patients. Materials and methods A prospective biomarker cohort study for total plasma circulating cell-free DNA (cfDNA) concentration was performed in 43 metastatic melanoma patients. For 38 patients, paired blood collections and scan assessments were available before treatment and at first response evaluation. Tumour burden was calculated as the sum of volumes from three-dimensional radiological measurements of all metastatic lesions in individual patients. Results Baseline cfDNA concentration correlated with pre-treatment tumour burden (ρ = 0.52, P < 0.001). Baseline cfDNA levels correlated significantly with hazard of death and overall survival, and a cut off value of 89 pg/μl identified two distinct prognostic groups (HR = 2.22 for high cfDNA, P = 0.004). Patients with cfDNA ≥89 pg/μl had shorter OS (10.0 versus 22.7 months, P = 0.009; HR = 2.22 for high cfDNA, P = 0.004) and the significance was maintained when compared with lactic dehydrogenase (LDH) in a multivariate analysis. We also found a correlation between the changes of cfDNA and treatment-related changes in tumour burden (ρ = 0.49, P = 0.002). In addition, the ratio between baseline cfDNA and tumour burden was prognostic (HR = 2.7 for cfDNA/tumour volume ≥8 pg/(μl*cm3), P = 0.024). Conclusions We have demonstrated that cfDNA is a surrogate marker of tumour burden in metastatic melanoma patients, and that it is prognostic for overall survival.Fil: Valpione, S.. University of Manchester; Reino Unido. Christie NHS Foundation Trust; Reino UnidoFil: Gremel, G.. University of Manchester; Reino UnidoFil: Mundra, P.. University of Manchester; Reino UnidoFil: Middlehurst, P.. University of Manchester; Reino UnidoFil: Galvani, E.. Christie NHS Foundation Trust; Reino Unido. University of Manchester; Reino UnidoFil: Girotti, Maria Romina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. University of Manchester; Reino UnidoFil: Lee, R.J.. University of Manchester; Reino UnidoFil: Garner, G.. University of Manchester; Reino UnidoFil: Dhomen, N.. University of Manchester; Reino UnidoFil: Lorigan, P.C.. Christie NHS Foundation Trust; Reino UnidoFil: Marais, R.. University of Manchester; Reino Unid

Brain microenvironment-driven resistance to immune and targeted therapies in acral melanoma.

BACKGROUND: Combination treatments targeting the MEK-ERK pathway and checkpoint inhibitors have improved overall survival in melanoma. Resistance to treatment especially in the brain remains challenging, and rare disease subtypes such as acral melanoma are not typically included in trials. Here we present analyses from longitudinal sampling of a patient with metastatic acral melanoma that became resistant to successive immune and targeted therapies. METHODS: We performed whole-exome sequencing and RNA sequencing on an acral melanoma that progressed on successive immune (nivolumab) and targeted (dabrafenib) therapy in the brain to identify resistance mechanisms. In addition, we performed growth inhibition assays, reverse phase protein arrays and immunoblotting on patient-derived cell lines using dabrafenib in the presence or absence of cerebrospinal fluid (CSF) in vitro. Patient-derived xenografts were also developed to analyse response to dabrafenib. RESULTS: Immune escape following checkpoint blockade was not due to loss of tumour cell recognition by the immune system or low neoantigen burden, but was associated with distinct changes in the microenvironment. Similarly, resistance to targeted therapy was not associated with acquired mutations but upregulation of the AKT/phospho-inositide 3-kinase pathway in the presence of CSF. CONCLUSION: Heterogeneous tumour interactions within the brain microenvironment enable progression on immune and targeted therapies and should be targeted in salvage treatments

A signal-seeking Phase 2 study of olaparib and durvalumab in advanced solid cancers with homologous recombination repair gene alterations

Purpose: To determine the safety and efficacy of PARP plus PD-L1 inhibition (olaparib + durvalumab, O + D) in patients with advanced solid, predominantly rare cancers harbouring homologous recombination repair (HRR) defects. Patients and methods: In total, 48 patients were treated with O + D, 16 with BRCA1/2 alterations (group 1) and 32 with other select HRR alterations (group 2). Overall, 32 (66%) patients had rare or less common cancers. The primary objective of this single-arm Phase II trial was a progression-free survival rate at 6 months (PFS6). Post hoc exploratory analyses were conducted on archival tumour tissue and serial bloods. Results: The PFS6 rate was 35% and 38% with durable objective tumour responses (OTR) in 3(19%) and 3(9%) in groups 1 and 2, respectively. Rare cancers achieving an OTR included cholangiocarcinoma, perivascular epithelioid cell (PEComa), neuroendocrine, gallbladder and endometrial cancer. O + D was safe, with five serious adverse events related to the study drug(s) in 3 (6%) patients. A higher proportion of CD38 high B cells in the blood and higher CD40 expression in tumour was prognostic of survival. Conclusions: O + D demonstrated no new toxicity concerns and yielded a clinically meaningful PFS6 rate and durable OTRs across several cancers with HRR defects, including rare cancers

Machine learning on normalized protein sequences

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.</p> <p>Findings</p> <p>We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.</p> <p>Conclusions</p> <p>We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.</p

GÊNEROS DISCURSIVOS E ENSINO: UMA PROPOSTA DE APLICAÇÃO EM SALA DE AULA

Os gêneros discursivos são formas de agir e interagir discursivamente e são inerentes à comunicação humana. Neste artigo, nos propomos, a partir de um percurso teórico, discutir sobre o conceito de gênero discursivo com base nas reflexões de Bakhtin (2000) e Marcuschi (2003, 2005), considerando sua aplicabilidade no ensino como condição para assegurar à construção de conhecimentos fundamentais para as práticas sociais de linguagem. Para isso, refletimos sobre o gênero discursivo como atividade sociocomunicativa de interação social, produzido para as necessidades de comunicação, constituído de componentes sociais, históricos, culturais e cognitivos. Além disso, analisamos a sequência didática na perspectiva de Dolz e Schneuwly (2004) como possibilidade de auxiliar o ensino através dos gêneros.&nbsp; Entendemos ser essencial, por essa razão, que as aulas de língua portuguesa centrem-se, nos diferentes níveis de ensino, nas dinâmicas sociais de interação por meio dos gêneros discursivos. &nbsp;&nbsp; &nbsp;&nbsp; &nbsp