Serum levels of matrix metalloproteinases-2 and-9 and their tissue inhibitors in inflammatory neuromuscular disorders

We monitored serum levels of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs) before and during intravenously applied immunoglobulin (IVIG) therapy in 33 patients with chronic immune-mediated neuropathies and myopathies and 15 controls. Baseline MMP-2 and TIMP-2 serum levels were lower and MMP-9 and TIMP-1 serum levels higher in all patients compared to age-matched controls. Eight days after IVIG treatment, MMP-2, TIMP-2, and TIMP-1 serum levels increased, while MMP-9 serum levels decreased, indicating tissue repair. After 60 days, MMP-9 levels increased, MMP-2 approached normal levels, while TIMP-1 and TIMP-2 serum levels were below day 8 levels, indicating relapsing tissue damage. Comparing the MMP/TIMP results with the clinical courses, IVIG treatment tended to change MMP/TIMP levels in a way that paralleled clinical improvement and relapse. In sum, during a distinct time period, IVIG therapy seems to be able to modulate VIMP-mediated tissue repair. Copyright (c) 2006 S. Karger AG, Basel

Collapse of a Bose gas: kinetic approach

We have analytically explored temperature dependence of critical number of particles for the collapse of a harmonically trapped attractively interacting Bose gas below the condensation point by introducing a kinetic approach within the Hartree-Fock approximation. The temperature dependence obtained by this easy approach is consisted with that obtained from the scaling theory.Comment: Brief Report, 4 pages, 1 figure, Accepted in Pramana-Journal of Physic

Spitzer Observations of Interstellar Object 1I/`Oumuamua

1I/`Oumuamua is the first confirmed interstellar body in our Solar System. Here we report on observations of `Oumuamua made with the Spitzer Space Telescope on 2017 November 21--22 (UT). We integrated for 30.2~hours at 4.5 micron (IRAC channel 2). We did not detect the object and place an upper limit on the flux of 0.3 uJy (3sigma). This implies an effective spherical diameter less than [98, 140, 440] meters and albedo greater than [0.2, 0.1, 0.01] under the assumption of low, middle, or high thermal beaming parameter eta, respectively. With an aspect ratio for `Oumuamua of 6:1, these results correspond to dimensions of [240:40, 341:57, 1080:180] meters, respectively. We place upper limits on the amount of dust, CO, and CO2 coming from this object that are lower than previous results; we are unable to constrain the production of other gas species. Both our size and outgassing limits are important because `Oumuamua's trajectory shows non-gravitational accelerations that are sensitive to size and mass and presumably caused by gas emission. We suggest that `Oumuamua may have experienced low-level post-perihelion volatile emission that produced a fresh, bright, icy mantle. This model is consistent with the expected eta value and implied high albedo value for this solution, but, given our strict limits on CO and CO2, requires another gas species --- probably H2O --- to explain the observed non-gravitational acceleration. Our results extend the mystery of `Oumuamua's origin and evolution

Consensus-Based Sorting of Neuronal Spike Waveforms

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data

Can we continue research in splenectomized dogs? Mycoplasma haemocanis: Old problem - New insight

We report the appearance of a Mycoplasma haemocanis infection in laboratory dogs, which has been reported previously, yet, never before in Europe. Outbreak of the disease was triggered by a splenectomy intended to prepare the dogs for a hemorrhagic shock study. The clinical course of the dogs was dramatic including anorexia and hemolytic anemia. Treatment included allogeneic transfusion, prednisone, and oxytetracycline. Systematic follow-up (n=12, blood smears, antibody testing and specific polymerase chain reaction) gives clear evidence that persistent eradication of M. haemocanis is unlikely. We, therefore, had to abandon the intended shock study. In the absence of effective surveillance and screening for M. haemocanis, the question arises whether it is prudent to continue shock research in splenectomized dogs. Copyright (C) 2004 S. Karger AG, Basel

Information Literacy Needs Open Access or: Open Access is not Only for Researchers

The Open Access was initially (blandly) conceived in view not only of researchers but also of lay readers, then this perspective slowly faded out. The Information Literacy movement wants to teach citizens how to arrive at trustable information but the amount of paywalled knowledge is still big. So, their lines of development are somehow complementary: Information Literacy needs Open Access for the citizens to freely access high quality information while Open Access truly fulfils its scope when it is conceived and realized not only for the researchers (an aristocratic view which was the initial one) but for the whole society

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants

BACKGROUND: Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. METHODS: A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. RESULTS: CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0–1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. CONCLUSION: State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide