Speeding stroke recovery? A systematic review of amphetamine after stroke

Introduction: The use of drugs to enhance recovery (“rehabilitation pharmacology”) has been assessed. Amphetamine can improve outcome in experimental models of stroke, and several small clinical trials have assessed its use in stroke. Methods: Electronic searches were performed to identify randomised controlled trials of amphetamine in stroke (ischaemic or haemorrhagic). Outcomes included functional outcome (assessed as combined death or disability/dependency), safety (death) and haemodynamic measures. Data were analysed as dichotomous or continuous outcomes, using odds ratios (OR), weighted or standardised mean difference, (WMD or SMD) using random-effects models with 95% confidence intervals (95% CI); statistical heterogeneity was assessed. Results: Eleven completed trials (n=329) were identified. Treatment with amphetamine was associated with non-significant trends to increased death (OR 2.78 (95% CI, 0.75– 10.23), n=329, 11 trials) and improved motor scores (WMD 3.28 (95% CI −0.48–7.04) n=257, 9 trials) but had no effect on the combined outcome of death and dependency (OR 1.15 (95% CI 0.65–2.06, n=206, 5 trials). Amphetamine increased systolic blood pressure (WMD 9.3 mmHg, 95% CI 3.3–15.3, n=106, 3 trials) and heart rate (WMD 7.6 beats per minute (bpm), 95% CI 1.8–13.4, n=106, 3 trials). Despite variations in treatment regimes, outcomes and follow-up duration there was no evidence of significant heterogeneity or publication bias. Conclusion: No evidence exists at present to support the use of amphetamine after stroke. Despite a trend to improved motor function, doubts remain over

Granulocyte-colony stimulating factor in experimental stroke and its effects on infarct size and functional outcome: a systematic review

Background Granulocyte-colony stimulating factor (G-CSF) shows promise as a treatment for stroke. This systematic review assesses G-CSF in experimental ischaemic stroke. Methods Relevant studies were identified with searches of Medline, Embase and PubMed. Data were extracted on stroke lesion size, neurological outcome and quality, and analysed using Cochrane Review Manager using random effects models; results are expressed as standardised mean difference (SMD) and odds ratio (OR). Results Data were included from 19 publications incorporating 666 animals. G-CSF reduced lesion size significantly in transient (SMD -1.63, p<0.00001) but not permanent (SMD -1.56, p=0.11) focal models of ischaemia. Lesion size was reduced at all doses and with treatment commenced within 4 hours of transient ischaemia. Neurological deficit (SMD -1.37, p=0.0004) and limb placement (SMD -1.88, p=0.003) improved with G-CSF; however, locomotor activity (>4 weeks post ischaemia) was not (SMD 0.76, p=0.35). Death (OR 0.27, p<0.0001) was reduced with G-CSF. Median study quality was 4 (range 0-7/8); Egger’s test suggested significant publication bias (p=0.001). Conclusions G-CSF significantly reduced lesion size in transient but not permanent models of ischaemic stroke. Motor impairment and death were also reduced. Further studies assessing dose-response, administration time, length of ischaemia and long-term functional recovery are needed.

Desarrollo de un modelo de calibración de pérdidas por riesgos inherentes a una Hipoteca Inversa. Métrica del riesgo de supervivencia.

391 p.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Aberrant c-MET activation impairs perinuclear actin cap organization with YAP1 cytosolic relocation

Growing evidence indicates that cell and nuclear deformability plays a crucial role in the determination of cancer cells tumorigenic and metastatic potential. The perinuclear actin cap, by wrapping the nucleus with a functional network of actomyosin cables, can modulate nuclear architecture and consequently cell/nuclear elasticity. The hepatocyte growth factor receptor (MET) stands out among other membrane receptors as crucial player of the actin filaments organization, but no data are available on a specific role for MET in the actin cap assembly and the overall nuclear architecture organization. In a cell system characterized by MET hyperactivation, we observed a strong rearrangement of the cellular actin caps, with a complete dismantling of apical stress fibers and a strikingly enhanced nuclear height. CRISPR/Cas9 silencing of MET completely reverted the aberrant phenotype, resulting in flattened cells with perfectly aligned perinuclear actomyosin bundles, as well as decreased MAPK and PI3K/AKT signaling, cell proliferation rate and aggressiveness. Interestingly, MET ablated cells acquired a remarkably directed and polarized migratory phenotype, contrarily to cells with MET sustained activation showing meandering random walk. A pathway enrichment analysis comparing MET-activated and MET-KO cells RNAseq data, unveiled the contribution of multiple pathways associated with cytoskeleton remodeling, regulation of cell shape and response to mechanical stimuli. In line, the co-transcriptional activator YAP1, playing a major role in cell mechanosensing and focal adhesions/actin stabilization, appeared the culprit of the genetic reassembling of KO cells. Indeed, MET silencing was shown to induce YAP1 nuclear shuttling and increased co-transcriptional activity. Finally, we were able to induce in a normal epithelial model a phenotype closer to MET activated cancer cells only by introducing a constitutive fusion protein of MET. Taken together, our results demonstrate a new mechanism of MET-mediated actin remodeling responsible for a tumor-initiating capacity and meandering random migration, which requires YAP1 inactivation.

Il filtro di Kalman: analisi di serie storiche e applicazione ai dati finanziari

Historia magistra vitae, scriveva Cicerone nel De Oratore; il passato deve insegnare a comprendere meglio il futuro. Un concetto che a primo acchito può sembrare confinato nell'ambito della filosofia e della letteratura, ma che ha invece applicazioni matematiche e fisiche di estrema importanza. Esistono delle tecniche che permettono, conoscendo il passato, di effettuare delle migliori stime del futuro? Esistono dei metodi che permettono, conoscendo il presente, di aggiornare le stime effettuate nel passato? Nel presente elaborato viene illustrato come argomento centrale il filtro di Kalman, un algoritmo ricorsivo che, dato un set di misure di una certa grandezza fino al tempo t, permette di calcolare il valore atteso di tale grandezza al tempo t+1, oltre alla varianza della relativa distribuzione prevista; permette poi, una volta effettuata la t+1-esima misura, di aggiornare di conseguenza valore atteso e varianza della distribuzione dei valori della grandezza in esame. Si è quindi applicato questo algoritmo, testandone l'efficacia, prima a dei casi fisici, quali il moto rettilineo uniforme, il moto uniformemente accelerato, l'approssimazione delle leggi orarie del moto e l'oscillatore armonico; poi, introducendo la teoria di Kendall conosciuta come ipotesi di random walk e costruendo un modello di asset pricing basato sui processi di Wiener, si è applicato il filtro di Kalman a delle serie storiche di rendimenti di strumenti di borsa per osservare se questi si muovessero effettivamente secondo un modello di random walk e per prevedere il valore al tempo finale dei titoli.

Conditional entropy and predictability for non-reversible Markov systems

The purpose of this thesis is to clarify the role of non-equilibrium stationary currents of Markov processes in the context of the predictability of future states of the system. Once the connection between the predictability and the conditional entropy is established, we provide a comprehensive approach to the definition of a multi-particle Markov system. In particular, starting from the well-known theory of random walk on network, we derive the non-linear master equation for an interacting multi-particle system under the one-step process hypothesis, highlighting the limits of its tractability and the prop- erties of its stationary solution. Lastly, in order to study the impact of the NESS on the predictability at short times, we analyze the conditional entropy by modulating the intensity of the stationary currents, both for a single-particle and a multi-particle Markov system. The results obtained analytically are numerically tested on a 5-node cycle network and put in correspondence with the stationary entropy production. Furthermore, because of the low dimensionality of the single-particle system, an analysis of its spectral properties as a function of the modulated stationary currents is performed.

Introducing CHIMeRA: from the development of a comprehensive biomedical database to the analysis of its sub-components.

In this thesis we discuss the expansion of an existing project, called CHIMeRA, which is a comprehensive biomedical network, and the analysis of its sub-components by using graph theory. We describe how it is structured internally, what are the existing databases from which it retrieves information and what machine learning techniques are used in order to produce new knowledge. We also introduce a new technique for graph exploration that is aimed to speed-up the network cover time under the condition that the analyzed graph is stellar; if this condition is satisfied, the improvement in the performance compared to the conventional exploration technique is extremely appealing. We show that the stellar structure is highly recurrent for sub-networks in CHIMeRA generated by queries, which made this technique even more interesting. Finally, we describe the convenience in using the CHIMeRA network for research purposes and what it could become in a very near future.

Optimal designs for the prediction in hierarchical random coefficient regression models

Magdeburg, Univ., Fak. für Mathematik, Diss., 2015

Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.

Impact of incorrect assumptions on the covariance structure of random effects and/or residuals in nonlinear mixed models for repeated measures data

In this paper we analyse, using Monte Carlo simulation, the possible consequences of incorrect assumptions on the true structure of the random effects covariance matrix and the true correlation pattern of residuals, over the performance of an estimation method for nonlinear mixed models. The procedure under study is the well known linearization method due to Lindstrom and Bates (1990), implemented in the nlme library of S-Plus and R. Its performance is studied in terms of bias, mean square error (MSE), and true coverage of the associated asymptotic confidence intervals. Ignoring other criteria like the convenience of avoiding over parameterised models, it seems worst to erroneously assume some structure than do not assume any structure when this would be adequate.

Identification of models using failure rate and mean residual life of doubly truncated random variables

In this paper, we study the relationship between the failure rate and the mean residual life of doubly truncated random variables. Accordingly, we develop characterizations for exponential, Pareto 11 and beta distributions. Further, we generalize the identities for fire Pearson and the exponential family of distributions given respectively in Nair and Sankaran (1991) and Consul (1995). Applications of these measures in file context of lengthbiased models are also explored

Parametric accelerated failure time models with random effects and an application to kidney transplant survival

Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney transplantation. Different combinations of the distribution of the random effects and baseline hazard function are considered and the fit of such models to the transplant data is critically assessed. A mixture model that combines short- and long-term components of a hazard function is then developed, which provides a more flexible model for the hazard function. The model can incorporate different explanatory variables and random effects in each component. The model is straightforward to fit using standard statistical software, and is shown to be a good fit to the transplant data. Copyright (C) 2004 John Wiley Sons, Ltd.