GeneNetTools: tests for Gaussian graphical models with shrinkage.

MOTIVATION: Gaussian graphical models (GGMs) are network representations of random variables (as nodes) and their partial correlations (as edges). GGMs overcome the challenges of high-dimensional data analysis by using shrinkage methodologies. Therefore, they have become useful to reconstruct gene regulatory networks from gene-expression profiles. However, it is often ignored that the partial correlations are 'shrunk' and that they cannot be compared/assessed directly. Therefore, accurate (differential) network analyses need to account for the number of variables, the sample size, and also the shrinkage value, otherwise, the analysis and its biological interpretation would turn biased. To date, there are no appropriate methods to account for these factors and address these issues. RESULTS: We derive the statistical properties of the partial correlation obtained with the Ledoit-Wolf shrinkage. Our result provides a toolbox for (differential) network analyses as (i) confidence intervals, (ii) a test for zero partial correlation (null-effects) and (iii) a test to compare partial correlations. Our novel (parametric) methods account for the number of variables, the sample size and the shrinkage values. Additionally, they are computationally fast, simple to implement and require only basic statistical knowledge. Our simulations show that the novel tests perform better than DiffNetFDR-a recently published alternative-in terms of the trade-off between true and false positives. The methods are demonstrated on synthetic data and two gene-expression datasets from Escherichia coli and Mus musculus. AVAILABILITY AND IMPLEMENTATION: The R package with the methods and the R script with the analysis are available in https://github.com/V-Bernal/GeneNetTools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The 'un-shrunk' partial correlation in Gaussian graphical models.

BACKGROUND: In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of nodes (representing the transcripts, metabolites or proteins) inter-connected by edges (reflecting their partial correlations). Learning the edges from quantitative molecular profiles is statistically challenging, as there are usually fewer samples than nodes ('high dimensional problem'). Shrinkage methods address this issue by learning a regularized GGM. However, it remains open to study how the shrinkage affects the final result and its interpretation. RESULTS: We show that the shrinkage biases the partial correlation in a non-linear way. This bias does not only change the magnitudes of the partial correlations but also affects their order. Furthermore, it makes networks obtained from different experiments incomparable and hinders their biological interpretation. We propose a method, referred to as 'un-shrinking' the partial correlation, which corrects for this non-linear bias. Unlike traditional methods, which use a fixed shrinkage value, the new approach provides partial correlations that are closer to the actual (population) values and that are easier to interpret. This is demonstrated on two gene expression datasets from Escherichia coli and Mus musculus. CONCLUSIONS: GGMs are popular undirected graphical models based on partial correlations. The application of GGMs to reconstruct regulatory networks is commonly performed using shrinkage to overcome the 'high-dimensional problem'. Besides it advantages, we have identified that the shrinkage introduces a non-linear bias in the partial correlations. Ignoring this type of effects caused by the shrinkage can obscure the interpretation of the network, and impede the validation of earlier reported results.

Angiogenic regulatory influence of extracellular matrix deposited by resting state asthmatic and non-asthmatic airway smooth muscle cells is similar.

The extracellular matrix (ECM) is the tissue microenvironment that regulates the characteristics of stromal and systemic cells to control processes such as inflammation and angiogenesis. Despite ongoing anti-inflammatory treatment, low levels of inflammation exist in the airways in asthma, which alters ECM deposition by airway smooth muscle (ASM) cells. The altered ECM causes aberrant behaviour of cells, such as endothelial cells, in the airway tissue. We therefore sought to characterize the composition and angiogenic potential of the ECM deposited by asthmatic and non-asthmatic ASM. After 72 hours under non-stimulated conditions, the ECM deposited by primary human asthmatic ASM cells was equal in total protein, collagen I, III and fibronectin content to that from non-asthmatic ASM cells. Further, the matrices of non-asthmatic and asthmatic ASM cells were equivalent in regulating the growth, activity, attachment and migration of primary human umbilical vein endothelial cells (HUVECs). Under basal conditions, asthmatic and non-asthmatic ASM cells intrinsically deposit an ECM of equivalent composition and angiogenic potential. Previous findings indicate that dysregulation of the airway ECM is driven even by low levels of inflammatory provocation. This study suggests the need for more effective anti-inflammatory therapies in asthma to maintain the airway ECM and regulate ECM-mediated aberrant angiogenesis.

Exact hypothesis testing for shrinkage-based Gaussian graphical models.

MOTIVATION: One of the main goals in systems biology is to learn molecular regulatory networks from quantitative profile data. In particular, Gaussian graphical models (GGMs) are widely used network models in bioinformatics where variables (e.g. transcripts, metabolites or proteins) are represented by nodes, and pairs of nodes are connected with an edge according to their partial correlation. Reconstructing a GGM from data is a challenging task when the sample size is smaller than the number of variables. The main problem consists in finding the inverse of the covariance estimator which is ill-conditioned in this case. Shrinkage-based covariance estimators are a popular approach, producing an invertible 'shrunk' covariance. However, a proper significance test for the 'shrunk' partial correlation (i.e. the GGM edges) is an open challenge as a probability density including the shrinkage is unknown. In this article, we present (i) a geometric reformulation of the shrinkage-based GGM, and (ii) a probability density that naturally includes the shrinkage parameter. RESULTS: Our results show that the inference using this new 'shrunk' probability density is as accurate as Monte Carlo estimation (an unbiased non-parametric method) for any shrinkage value, while being computationally more efficient. We show on synthetic data how the novel test for significance allows an accurate control of the Type I error and outperforms the network reconstruction obtained by the widely used R package GeneNet. This is further highlighted in two gene expression datasets from stress response in Eschericha coli, and the effect of influenza infection in Mus musculus. AVAILABILITY AND IMPLEMENTATION: https://github.com/V-Bernal/GGM-Shrinkage. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Relationship between technology acceptance model, self-regulation strategies, and academic self-efficacy with academic performance and perceived learning among college students during remote education.

Introduction: The aim of this study was to examine the relationship between the technology acceptance model, self-regulation strategies, and academic self-efficacy with academic performance and perceived learning among college students during remote education. Methods: The participants were 301 university students from Lima. Structural equation model was used to test the proposed theoretical relationships between the variables. On the one hand, the study sought to explore the relationship between academic self-efficacy and self-regulation strategies with the technology acceptance model. On the other hand, it sought to determine whether the three dimensions of the technology acceptance model are positively related to perceived learning and academic performance. Results: The results suggest the importance of improving psychological variables such as self-efficacy and self-regulation strategies to improve the acceptance of technology, which would also improve the academic performance and perceived learning of students in a virtual environment. Discussion: The discussion highlights the significance of self-efficacy and metacognitive strategies in influencing technology perception and attitudes, ultimately impacting perceived learning and academic performance in virtual education.

Classifying and tracking rehabilitation interventions through machine-learning algorithms in individuals with stroke.

INTRODUCTION: Stroke is the leading cause of disability worldwide. It has been well-documented that rehabilitation (rehab) therapy can aid in regaining health and function for individuals with stroke. Yet, tracking in-home rehab continues to be a challenge because of a lack of resources and population-scale demands. In order to address this gap, we implemented a methodology to classify and track rehab interventions in individuals with stroke. METHODS: We developed personalized classification algorithms, including neural network-based algorithms, to classify four rehab exercises performed by two individuals with stroke who were part of a week-long therapy camp in Jamaica, a low- and middle-income country. Accelerometry-based wearable sensors were placed on each upper and lower limb to collect movement data during therapy. RESULTS: The classification accuracy for traditional and neural network-based algorithms utilizing feature data (e.g., number of peaks) from the sensors ranged from 64 to 94%, respectively. In addition, the study proposes a new method to assess change in bilateral mobility over the camp duration. CONCLUSION: The results of this pilot study indicate that personalized supervised learning algorithms can be used to classify and track rehab activities and functional outcomes in resource limited settings such as LMICs.

The sputum transcriptome better predicts COPD exacerbations after the withdrawal of inhaled corticosteroids than sputum eosinophils.

INTRODUCTION: Continuing inhaled corticosteroid (ICS) use does not benefit all patients with COPD, yet it is difficult to determine which patients may safely sustain ICS withdrawal. Although eosinophil levels can facilitate this decision, better biomarkers could improve personalised treatment decisions. METHODS: We performed transcriptional profiling of sputum to explore the molecular biology and compared the predictive value of an unbiased gene signature versus sputum eosinophils for exacerbations after ICS withdrawal in COPD patients. RNA-sequencing data of induced sputum samples from 43 COPD patients were associated with the time to exacerbation after ICS withdrawal. Expression profiles of differentially expressed genes were summarised to create gene signatures. In addition, we built a Bayesian network model to determine coregulatory networks related to the onset of COPD exacerbations after ICS withdrawal. RESULTS: In multivariate analyses, we identified a gene signature (LGALS12, ALOX15, CLC, IL1RL1, CD24, EMR4P) associated with the time to first exacerbation after ICS withdrawal. The addition of this gene signature to a multiple Cox regression model explained more variance of time to exacerbations compared to a model using sputum eosinophils. The gene signature correlated with sputum eosinophil as well as macrophage cell counts. The Bayesian network model identified three coregulatory gene networks as well as sex to be related to an early versus late/nonexacerbation phenotype. CONCLUSION: We identified a sputum gene expression signature that exhibited a higher predictive value for predicting COPD exacerbations after ICS withdrawal than sputum eosinophilia. Future studies should investigate the utility of this signature, which might enhance personalised ICS treatment in COPD patients.

Gene network approach reveals co-expression patterns in nasal and bronchial epithelium.

Nasal gene expression profiling is a new approach to investigate the airway epithelium as a biomarker to study the activity and treatment responses of obstructive pulmonary diseases. We investigated to what extent gene expression profiling of nasal brushings is similar to that of bronchial brushings. We performed genome wide gene expression profiling on matched nasal and bronchial epithelial brushes from 77 respiratory healthy individuals. To investigate differences and similarities among regulatory modules, network analysis was performed on correlated, differentially expressed and smoking-related genes using Gaussian Graphical Models. Between nasal and bronchial brushes, 619 genes were correlated and 1692 genes were differentially expressed (false discovery rate <0.05, |Fold-change|>2). Network analysis of correlated genes showed pro-inflammatory pathways to be similar between the two locations. Focusing on smoking-related genes, cytochrome-P450 pathway related genes were found to be similar, supporting the concept of a detoxifying response to tobacco exposure throughout the airways. In contrast, cilia-related pathways were decreased in nasal compared to bronchial brushes when focusing on differentially expressed genes. Collectively, while there are substantial differences in gene expression between nasal and bronchial brushes, we also found similarities, especially in the response to the external factors such as smoking.

Improved neurotensin-vector-mediated gene transfer by the coupling of hemagglutinin HA2 fusogenic peptide and Vp1 SV40 nuclear localization signal.

Recently we reported that neurotensin-SPDP-poly-L-lysine (NT-vector) is able to bind plasmid DNA (NT-polyplex) and polyfect cells expressing the high-affinity neurotensin receptor (NTRH) although with low transfecting efficiency: in vitro, 6.5+/-1.5%, and in vivo, 5+/-4%. In this work, we attempted to increase the transfecting efficiency by integrating the hemagglutinin HA2 fusogenic peptide and the Vp1 nuclear localization signal of SV40 to the NT-polyplex (fusogenic-karyophilic-NT-polyplex). Confocal microscopy and flow cytometry analysis showed that the fusogenic-karyophilic-NT-polyplex produced mostly nuclear localization of the plasmid DNA in NTRH-bearing N1E-115 cells. About 50% of N1E-115 cells internalized and expressed the reporter gene when the plasmid DNA was transferred by the fusogenic-karyophilic-NT-polyplex. Although to a less extent, the addition of each viral peptide separately to NT-polyplex (fusogenic-NT-polyplex or karyophilic-NT-polyplex) improved polyfection. Fusogenic-NT-polyplex produced 22.41+/-5.96% of internalization and 20.35+/-0.82% of expression in N1E-115 cells, whereas karyophilic-NT-polyplex yielded 13.75+/-3.88% and 10.94+/-2.04%, respectively. Basal internalization and expression were detected in N1E-115 cells in the presence of 100 nM SR-48692 and in NTRH-lacking cells. The fusogenic-karyophilic-NT-polyplex was microinjected into the substantia nigra to test its ability for gene transfer in vivo. Fusogenic-karyophilic-NT-polyplex internalization was observed within dopamine neurons only. Reporter gene expression was observed in a high proportion of dopamine neurons up to 60 days after NT-polyfection. Both internalization and expression were prevented by SR-48692. Our results show that the fusogenic-karyophilic-NT-polyplex is a highly efficient and specific gene vector and encourage its use to transfer gene of physiological interest to NTRH-bearing neurons.

Reacciones adversas: medicamentos y hematología / Adverse reactions: drugs and hematology

El tratamiento de las enfermedades requiere de un uso riguroso y adecuado de fármacos cuando éstos sean realmente necesartios y apropiados a las necesidades clínicas del paciente. El tratamiento puede ser farmacológico o no farmacológico. En caso de optar por un tratamiento farmacológico, este debe ser prescrito en una dosificación correcta, en la presentación correcta, debe respondera requerimientos individuales, por un período adecuado, en tiempo apropiado, administrado correctamente y al costo más conveniente para el paciente y para la comunidad. Es, fundamental que el medicamento sea de eficacia comprabada, efectivo, de calidad verificada, con amplio margen de seguridad y beneficio aceptable. Asímismo se debe considerar, no sólo la disponibilidad de los medicamentos prescritos, sino su accesibilidad y las condiciones de sistemas de abastecimiento. El presente estudio examina algunos de estos importantes aspectos, tomando como caso estudio, algunos medicamentos, frecuentemente utilizados y sus cosecuencias y efectos colaterales en el area de hematología

Atención de urgencias por lesiones: emergencia del Hospital Central de Sanidad de las Fuerzas Policiales / Traumatic lesions in the Emergency Unit of the Police Hospital

Se han revisado 132,900 pacientes atendidos durante los años 1980 a 1982 en la División de Emergencia del Hospital Central de Sanidad de las Fuerzas Policiales. El 112% de la demanda correspondió a urgencias por lesiones con predominio de 3 a 1 en varones, los que, en 50% de casos, fueron personal policial en actividad. Las extremidades fueron el segmento más comprometido pero las lesiones de cabeza y tórax reunieron la mayor peligrosidad especialmente los producidos por proyectiles y por accidente de tránsito. La contusión fué el tipo de daño más frecuente observándose una mortalidad creciente por lesiones por arma de fuego incluyendo artefactos explosivos