Microparticle Shedding from Neural Progenitor Cells and Vascular Compartment Cells Is Increased in Ischemic Stroke

Purpose

Ischemic stroke has shown to induce platelet and endothelial microparticle shedding, but whether stroke induces microparticle shedding from additional blood and vascular compartment cells is unclear. Neural precursor cells have been shown to replace dying neurons at sites of brain injury; however, if neural precursor cell activation is associated to microparticle shedding, and whether this activation is maintained at long term and associates to stroke type and severity remains unknown. We analyzed neural precursor cells and blood and vascular compartment cells microparticle shedding after an acute ischemic stroke.

Methods

Forty-four patients were included in the study within the first 48h after the onset of stroke. The cerebral lesion size was evaluated at 3–7 days of the stroke. Circulating microparticles from neural precursor cells and blood and vascular compartment cells (platelets, endothelial cells, erythrocytes, leukocytes, lymphocytes, monocytes and smooth muscle cells) were analyzed by flow cytometry at the onset of stroke and at 7 and 90 days. Forty-four age-matched high cardiovascular risk subjects without documented vascular disease were used as controls.

Results

Compared to high cardiovascular risk controls, patients showed higher number of neural precursor cell- and all blood and vascular compartment cell-derived microparticles at the onset of stroke, and after 7 and 90 days. At 90 days, neural precursor cell-derived microparticles decreased and smooth muscle cell-derived microparticles increased compared to levels at the onset of stroke, but only in those patients with the highest stroke-induced cerebral lesions.

Conclusions

Stroke increases blood and vascular compartment cell and neural precursor cell microparticle shedding, an effect that is chronically maintained up to 90 days after the ischemic event. These results show that stroke induces a generalized blood and vascular cell activation and the initiation of neuronal cell repair process after stroke. Larger cerebral lesions associate with deeper vessel injury affecting vascular smooth muscle cells.     

Maternal Serum C-Reactive Protein in Women with Preterm Prelabor Rupture of Membranes

Objective

This study evaluated maternal C-reactive protein (CRP) as a predictor of microbial invasion of the amniotic cavity (MIAC) and histological chorioamnionitis (HCA) in women with preterm prelabor rupture of the membranes (PPROM) before and after 32 weeks of gestation.

Methods

This study was a prospective observational cohort study of 386 women. Maternal serum CRP concentrations were evaluated, and amniotic fluid samples were obtained via transabdominal amniocentesis at the time of admission. Placentas underwent histopathological examination after delivery. MIAC was defined based on a positive PCR for Ureaplasma species, Mycoplasma hominis and Chlamydia trachomatis and/or positive 16S rRNA gene amplification. HCA was defined based on the Salafia classification.

Results

Maternal CRP was significantly higher in women with MIAC and HCA (median 9.0 mg/l) than in women with HCA alone (median 6.9 mg/l), MIAC alone (median 7.4 mg/l) and without MIAC or HCA (median 4.5 mg/l) (p<0.0001). CRP was a weak predictor of the occurrence of MIAC and HCA before and after 32 weeks of gestation. Only the 95^th percentile of CRP and PPROM before 32 weeks exhibited a false-positive rate of 1%, a positive predictive value of 90% and a positive likelihood ratio of 13.2 to predict MIAC and HCA. However, the low sensitivity of 15% limits the clinical utility of this detection.

Conclusion

CRP is a poor predictor of the occurrence of MIAC and HCA, even at early gestational ages.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

Comparative proteomic analysis of growth hormone secretagogue A233 treatment of murine macrophage cells J774A.2 indicates it has a role in antiviral innate response

BackgroundGrowth hormone secretagogues (GHS), among other factors, regulate the release of GH. The biological activity of the secretagogue peptide A233 as a promoter of growth and innate immunity in teleost fish has previously been demonstrated, but its role in the immune system of mammals is not well understood.MethodsThe effect of the peptide was investigated in J774A.2 macrophage cells using a comparative proteomics approach after 6 and 12 h of peptide stimulation.ResultsThe functional analysis of differentially modulated proteins showed that A233 peptide treatment appears to promote activation and ROS-dependent cytotoxic functions in macrophages and enhanced expression of antiviral protein complexes such as MAVS. In accordance with this hypothesis, we found that A233 treatment enhanced superoxide anion production and the IFN-γ level in J774A.2 cells and mouse splenocytes, respectively, and reduced viral load in a dengue virus mouse model of infection.ConclusionsThe growth hormone secretagogue A233 peptide promotes activation of ROS-dependent cytotoxic functions and exerts immunomodulatory effects that enable an antiviral state in a dengue virus mouse model.General SignificanceThe increase of IFN-γ level and the differential modulation of antiviral proteins by the A233 peptide suggest that the molecule could activate an innate immune response with a possible further impact in the treatment of acute and chronic diseases.     

A taxonomic revision of <Emphasis Type="Italic">Bursera</Emphasis> subgen. <Emphasis Type="Italic">Bursera</Emphasis> in the Greater Antilles and the Bahamas,including a new species from Cuba

A revision of Bursera in the Greater Antilles and the Bahamas has confirmed that Commiphora does not occur in the region and that all but one species in the region belong to Bursera subgen. Bursera. Here we describe a new species, Bursera yaterensis; B. nashii is synonymized with B. glauca and B. ovata is synonymized with B. trinitensis; and we return five species from Commiphora to Bursera. A dichotomous key is provided using mostly vegetative characters due to the frequent lack of adequate reproductive material and the relative uniformity of most floral and fruit characters.