Using Variable and Fixed Topological Indices for the Prediction of Reaction Rate Constants of Volatile Unsaturated Hydrocarbons with OH Radicals

mVolatile organic compounds (VOCs) play an important role in differentphotochemical processes in the troposphere. In order to predict their impact on ozoneformation processes a detailed knowledge about their abundance in the atmosphere as wellas their reaction rate constants is required. The QSPR models were developed for theprediction of reaction rate constants of volatile unsaturated hydrocarbons. The chemicalstructure was encoded by constitutional and topological indices. Multiple linear regressionmodels using CODESSA software was developed with the RMSCV error of 0.119 log units.The chemical structure was encoded by six topological indices. Additionally, a regressionmodel using a variable connectivity index was developed. It provided worse cross-validation results with an RMSCV error of 0.16 log units, but enabled a structuralinterpretation of the obtained model. We differentiated between three classes of carbonatoms: sp2-hybridized, non-allylic sp3-hybridized and allylic sp3-hybridized. The structuralinterpretation of the developed model shows that most probably the most importantmechanisms are the addition to multiple bonds and the hydrogen atom abstraction at allylicsites

Emergence and fate of stem cell-like Tcf7⁺ CD8⁺ T cells during a primary immune response to viral infection.

In response to infection, naïve CD8 <sup>+</sup> T (T <sub>N</sub> ) cells yield a large pool of short-lived terminal effector (T <sub>TE</sub> ) cells that eliminate infected host cells. In parallel, a minor population of stem cell-like central memory (T <sub>CM</sub> ) cells forms, which has the capacity to maintain immunity after pathogen clearance. It has remained uncertain whether stem-like T <sub>CM</sub> cells arise by dedifferentiation from a subset of cytolytic T <sub>TE</sub> cells or whether priming generates stem-like cells capable of seeding the T <sub>CM</sub> compartment and, if so, when cytolytic T <sub>TE</sub> cells branch off. Here, we show that CD8 <sup>+</sup> T cells with stem-like properties, which are identified by the expression of TCF1 (encoded by Tcf7), are present across the primary response to infection. Priming programs T <sub>N</sub> cells to undergo multiple cell divisions, over the course of which TCF1 expression is maintained. These TCF1 <sup>+</sup> cells further expand relatively independently of systemic inflammation, antigen dose, or affinity, and they quantitatively yield TCF1 <sup>+</sup> T <sub>CM</sub> cells after pathogen clearance. Inflammatory signals suppress TCF1 expression in early divided TCF1 <sup>+</sup> cells. TCF1 down-regulation is associated with the irreversible loss of self-renewal capacity and the silencing of stem/memory genes, which precedes the stable acquisition of a T <sub>TE</sub> state. TCF1 expression restrains cell cycling, explaining in part the limited expansion of TCF1 <sup>+</sup> relative to TCF1 <sup>-</sup> cells during the primary response. Thus, our data are consistent with terminal differentiation of effector cells being a step-wise process that is initiated by inflammation in primed stem-like cells, which would otherwise become central memory cells by default

Luminescent properties of Bi-doped polycrystalline KAlCl4

We observed an intensive near-infrared luminescence in Bi-doped KAlCl4 polycrystalline material. Luminescence dependence on the excitation wavelength and temperature of the sample was studied. Our experimental results allow asserting that the luminescence peaked near 1 um belongs solely to Bi+ ion which isomorphically substitutes potassium in the crystal. It was also demonstrated that Bi+ luminescence features strongly depend on the local ion surroundings

Non-alcoholic fatty liver disease as a risk factor for anemia of chronic inflammation (experimental research)

The aim of the study. In recent years, non-alcoholic fatty liver disease (NAFLD) has been considered a hepatic manifestation of the metabolic syndrome. The main consequence of NAFLD is chronic hepatic inflammation, which leads to dyslipidemia, inflammation, increased oxidative stress, and endothelial dysfunction. Immune activation in response to interaction with agents of a metabolic nature induces the release of pro-inflammatory cytokines in the liver, which subsequently cause iron сhomeostasis disorder. This leads to a frequent association of NAFLD with anemia of various etiology. In this regard, we considered it important to assess the severity of the systemic inflammatory response in NAFLD in the experiment in order to -diagnose anemia of chronic inflammation.Materials and methods. The study was carried out on 26 male Wistar rats, which were divided into control and experimental groups. In animals of the experimental group, NAFLD was modeled according to the generally accepted method. In order to assess metabolic disorders, we determined the main biochemical parameters, a complete blood count with the calculation of erythrocyte indices, the concentration of the main pro-inflammatory cytokines – interleukin (IL) 1, IL-6. Results. In laboratory rats with NAFLD, a statistically significant increase of intrahepatic enzymes in blood serum was found. The state of the erythrocyte lineage of hematopoiesis in the experimental group progressively worsened and caused the development of anemic syndrome. Synchronously, a statistically significant increase in serum levels of IL-1, IL-6 was recorded, which confirms the correlation of NAFLD with anemia of chronic inflammation.Conclusions. A high concentration of IL-1, IL-6 cytokines in NAFLD inhibits iron absorption in the duodenum, leads to the activation of macrophages, blocking the release of iron processed from aging erythrocytes into plasma. Further study of the mechanisms of anemia in NAFLD provides important therapeutic targets in the treatment of both NAFLD and its comorbidities

Short-course antibiotic therapy for critically ill patients treated for postoperative intra-abdominal infection: the DURAPOP randomised clinical trial

PURPOSE: Shortening the duration of antibiotic therapy (ABT) is a key measure in antimicrobial stewardship. The optimal duration of ABT for treatment of postoperative intra-abdominal infections (PIAI) in critically ill patients is unknown. METHODS: A multicentre prospective randomised trial conducted in 21 French intensive care units (ICU) between May 2011 and February 2015 compared the efficacy and safety of 8-day versus 15-day antibiotic therapy in critically ill patients with PIAI. Among 410 eligible patients (adequate source control and ABT on day 0), 249 patients were randomly assigned on day 8 to either stop ABT immediately (n = 126) or to continue ABT until day 15 (n = 123). The primary endpoint was the number of antibiotic-free days between randomisation (day 8) and day 28. Secondary outcomes were death, ICU and hospital length of stay, emergence of multidrug-resistant (MDR) bacteria and reoperation rate, with 45-day follow-up. RESULTS: Patients treated for 8 days had a higher median number of antibiotic-free days than those treated for 15 days (15 [6-20] vs 12 [6-13] days, respectively; P < 0.0001) (Wilcoxon rank difference 4.99 days [95% CI 2.99-6.00; P < 0.0001). Equivalence was established in terms of 45-day mortality (rate difference 0.038, 95% CI - 0.013 to 0.061). Treatments did not differ in terms of ICU and hospital length of stay, emergence of MDR bacteria or reoperation rate, while subsequent drainages between day 8 and day 45 were observed following short-course ABT (P = 0.041). CONCLUSION: Short-course antibiotic therapy in critically ill ICU patients with PIAI reduces antibiotic exposure. Continuation of treatment until day 15 is not associated with any clinical benefit. CLINICALTRIALS. GOV IDENTIFIER: NCT01311765

Inferring the role of transcription factors in regulatory networks

<p>Abstract</p> <p>Background</p> <p>Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple organisms up to higher eukaryotes. Admittedly, a key ingredient in developing a reconstruction method is its ability to integrate heterogeneous sources of information, as well as to comply with practical observability issues: measurements can be scarce or noisy. In this work, we show how to combine a network of genetic regulations with a set of expression profiles, in order to infer the functional effect of the regulations, as inducer or repressor. Our approach is based on a consistency rule between a network and the signs of variation given by expression arrays.</p> <p>Results</p> <p>We evaluate our approach in several settings of increasing complexity. First, we generate artificial expression data on a transcriptional network of <it>E. coli </it>extracted from the literature (1529 nodes and 3802 edges), and we estimate that 30% of the regulations can be annotated with about 30 profiles. We additionally prove that at most 40.8% of the network can be inferred using our approach. Second, we use this network in order to validate the predictions obtained with a compendium of real expression profiles. We describe a filtering algorithm that generates particularly reliable predictions. Finally, we apply our inference approach to <it>S. cerevisiae </it>transcriptional network (2419 nodes and 4344 interactions), by combining ChIP-chip data and 15 expression profiles. We are able to detect and isolate inconsistencies between the expression profiles and a significant portion of the model (15% of all the interactions). In addition, we report predictions for 14.5% of all interactions.</p> <p>Conclusion</p> <p>Our approach does not require accurate expression levels nor times series. Nevertheless, we show on both data, real and artificial, that a relatively small number of perturbation experiments are enough to determine a significant portion of regulatory effects. This is a key practical asset compared to statistical methods for network reconstruction. We demonstrate that our approach is able to provide accurate predictions, even when the network is incomplete and the data is noisy.</p

Physiochemical property space distribution among human metabolites, drugs and toxins

<p>Abstract</p> <p>Background</p> <p>The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.</p> <p>Results</p> <p>We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.</p> <p>Conclusion</p> <p>The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.</p