Role of FeNO in predicting the responsiveness of inhaled corticosteroids in COPD: a systematic review.

BACKGROUND: Fractional exhaled nitric oxide (FeNO) as a predictor of inhaled corticosteroid (ICS) response in asthma has been established. However, the same has not been established in chronic obstructive pulmonary disease (COPD). An optimal value of FeNO for prescribing and monitoring ICS response has not been quantified. AIM: To examine the evidence for this association. METHOD: A systematic review was conducted of randomised controlled trials and observational studies examining the association between FeNO level and response to ICS in COPD patients. All studies examining this association were included. Five databases were searched thoroughly. Systematic screening, full-text reviews, and data extraction were carried out based on eligibility criteria. RESULTS: A total of 8690 studies were identified, 342 texts were screened fully, and six studies were included for the final review. One was a randomised controlled trial and the other five were non-randomised interventional trials. One study was conducted in asthma-COPD overlap (ACO patients). After ICS use, three studies found statistically significant correlations between FeNO and lung function improvement (FEV1), and three studies also found significant correlations between FeNO and COPD quality-of-life scores. CONCLUSION: Measurement of FeNO is non-invasive and standardised, with results available at the point of testing. Because of the small sample size and short duration of studies, exacerbation frequencies were not measured. Despite this, the review suggests that FeNO may be a potential biomarker for assessing ICS response in COPD. Further research that stratifies patients by FeNO levels and assesses the impact on acute exacerbations is needed to understand its potential value in routine clinical practice.

Multimodal interactions drive chromatin phase separation and compaction.

Gene silencing is intimately connected to DNA condensation and the formation of transcriptionally inactive heterochromatin by Heterochromatin Protein 1α (HP1α). Because heterochromatin foci are dynamic and HP1α can promote liquid-liquid phase separation, HP1α-mediated phase separation has been proposed as a mechanism of chromatin compaction. The molecular basis of HP1α-driven phase separation and chromatin compaction and the associated regulation by trimethylation of lysine 9 in histone 3 (H3K9me3), which is the hallmark of constitutive heterochromatin, is however largely unknown. Using a combination of chromatin compaction and phase separation assays, site-directed mutagenesis, and NMR-based interaction analysis, we show that human HP1α can compact chromatin in the absence of liquid-liquid phase separation. We further demonstrate that H3K9-trimethylation promotes compaction of chromatin arrays through multimodal interactions. The results provide molecular insights into HP1α-mediated chromatin compaction and thus into the role of human HP1α in the regulation of gene silencing.

Synergistic Effects of Limosilactobacillus fermentum ASBT-2 with Oxyresveratrol Isolated from Coconut Shell Waste.

Value-added phytochemicals from food by-products and waste materials have gained much interest and among them, dietary polyphenolic compounds with potential biological properties extend a promising sustainable approach. Oxyresveratrol (Oxy), a stilbenoid polyphenol, possesses great therapeutic potential, though its pharmacokinetic issues need attention. A good source of oxyresveratrol was found in underutilized coconut shells and the synbiotic applications of the compound in combination with a potential probiotic isolate Limosilactobacillus fermentum ASBT-2 was investigated. The compound showed lower inhibitory effects on the strain with minimum inhibitory concentration (MIC) of 1000 µg/mL. Oxyresveratrol at sub-MIC concentrations (500 µg/mL and 250 µg/mL) enhanced the probiotic properties without exerting any inhibitory effects on the strain. The combination at sub- MIC concentration of the compound inhibited Salmonella enterica and in silico approaches were employed to elucidate the possible mode of action of oxy on the pathogen. Thus, the combination could target pathogens in the gut without exerting negative impacts on growth of beneficial strains. This approach could be a novel perspective to address the poor pharmacokinetic properties of the compound.

Modeling the Effect of Environmental Geometries on Grid Cell Representations.

Grid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern is originally considered to be independent of the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al., 2015) and in connected environments (Carpenter et al., 2015). Aforementioned experimental results demonstrated the dependence of grid cell activity on environmental geometry. Analysis of grid cell periodicity on practically infinite variations of environmental geometry imposes a limitation on the experimental study. Hence we analyze the dependence of grid cell periodicity on the environmental geometry purely from a computational point of view. We use a hierarchical oscillatory network model where velocity inputs are presented to a layer of Head Direction cells, outputs of which are projected to a Path Integration layer. The Lateral Anti-Hebbian Network (LAHN) is used to perform feature extraction from the Path Integration neurons thereby producing a spectrum of spatial cell responses. We simulated the model in five types of environmental geometries such as: (1) connected environments, (2) convex shapes, (3) concave shapes, (4) regular polygons with varying number of sides, and (5) transforming environment. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model encode not just the local position but also more global information like the shape of the environment. Furthermore, the model is able to capture the invariant attributes of the physical space ingrained in its LAHN layer, thereby revealing its ability to classify an environment using this information. The proposed model is interesting not only because it is able to capture the experimental results but, more importantly, it is able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity and suggesting the possibility of grid cells encoding the invariant properties of an environment.