Optimized isolation of 7,7′-biphyscion starting from Cortinarius rubrophyllus, a chemically unexplored fungal species rich in photosensitizers

Mushrooms such as the dermocyboid Cortinarius rubrophyllus are characterized by strikingly colorful fruiting bodies. The molecular dyes responsible for such colors recently experienced a comeback as photoactive compounds with remarkable photophysical and photobiological properties. One of them-7,7'-biphyscion-is a dimeric anthraquinone that showed promising anticancer effects in the low nanomolar range under blue-light irradiation. Compared to acidic anthraquinones, 7,7'-biphyscion was more efficiently taken up by cells and induced apoptosis after photoactivation. However, seasonal collection of mushrooms producing this compound, low extraction yields, and tricky fungal identification hamper further developments to the clinics. To bypass these limitations, we demonstrate here an alternative approach utilizing a precursor of 7,7'-biphyscion, i.e., the pre-anthraquinone flavomannin-6,6'-dimethyl ether, which is abundant in many species of the subgenus Dermocybe. Controlled oxidation of the crude extract significantly increased the yield of 7,7'-biphyscion by 100%, which eased the isolation process. We also present the mycochemical and photobiological characterization of the yet chemically undescribed species, i.e. C. rubrophyllus. In total, eight pigments (1-8) were isolated, including two new glycosylated anthraquinones (1 and 2). Light-dependent generation of singlet oxygen was detected for the first time for emodin-1-O-beta-D-glucopyranoside (3) [photophysical measurement: Phi(Delta) =0.11 (CD3OD)]. Furthermore, emodin (7) was characterized as promising compound in the photocytotoxicity assay with EC50-values in the low micromolar range under irradiation against cells of the cancer cell lines AGS, A549, and T24.Metals in Catalysis, Biomimetics & Inorganic Material

A Revised Design for Microarray Experiments to Account for Experimental Noise and Uncertainty of Probe Response

Background Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. Conclusion The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification. The software platform MaxDIA streamlines analysis of data-independent acquisition proteomics

Automated analysis of digital fundus autofluorescence images of geographic atrophy in advanced age-related macular degeneration using confocal scanning laser ophthalmoscopy (cSLO)

BACKGROUND: Fundus autofluorescence (AF) imaging using confocal scanning laser ophthalmoscopy (cSLO) provides an accurate delineation of areas of geographic atrophy (GA). Automated computer-assisted methods for detecting and removing interfering vessels are needed to support the GA quantification process in longitudinal studies and in reading centres. METHODS: A test tool was implemented that uses region-growing techniques to segment GA areas. An algorithm for illuminating shadows can be used to process low-quality images. Agreement between observers and between three different methods was evaluated by two independent readers in a pilot study. Agreement and objectivity were assessed using the Bland-Altman approach. RESULTS: The new method (C) identifies vascular structures that interfere with the delineation of GA. Results are comparable to those of two commonly used procedures (A, B), with a mean difference between C and A of -0.67 mm(2 )(95% CI [-0.99, -0.36]), between B and A of -0.81 mm(2), (95% CI [-1.08, -0.53]), and between C and B of 0.15 mm(2 )(95% CI [-0.12, 0.41]). Objectivity of a method is quantified by the mean difference between observers: A 0.30 mm(2 )(95% CI [0.02, 0.57]), B -0.11 mm(2 )(95% CI [-0.28, 0.10]), and C 0.12 mm(2 )(95% CI [0.02, 0.22]). CONCLUSION: The novel procedure is comparable with regard to objectivity and inter-reader agreement to established methods of quantifying GA. It considerably speeds up the lengthy measurement process in AF with well defined GA zones

Learning from Sustainability-Oriented Innovation

This chapter argues that insights from the realm of sustainability-oriented innovation can provide useful answers to the question of why Small and Medium-Sized Enterprises (SMEs) would (or should) become interested in implementing responsible innovation practices. It is based on the assumption that “responsible innovation” and “sustainability-oriented innovation” are different approaches aimed at orienting innovation towards increased positive impacts on social and natural environments. Motivations and influences for pursuing sustainability-oriented inno-vation have been studied in the past, and can provide insights into reasons for pursu-ing the implementation of responsible innovation practices

Heart rate monitoring on the stroke unit. What does heart beat tell about prognosis? An observational study

<p>Abstract</p> <p>Background</p> <p>Guidelines recommend maintaining the heart rate (HR) of acute stroke patients within physiological limits; data on the frequency and predictors of significant deviations from these limits are scarce.</p> <p>Methods</p> <p>Demographical data, stroke risk factors, NIH stroke scale score, lesion size and location, and ECG parameters were prospectively assessed in 256 patients with ischemic stroke. Patients were continuously monitored for at least 24 hours on a certified stroke unit. Tachycardia (HR ≥120 bpm) and bradycardia (HR <45 bpm) and cardiac rhythm (sinus rhythm or atrial fibrillation) were documented. We investigated the influence of risk factors on HR disturbances and their respective influence on dependence (modified Rankin Scale ≥ 3 after three months) and mortality.</p> <p>Results</p> <p>HR ≥120 bpm occurred in 39 patients (15%). Stroke severity (larger lesion size/higher NIHSS-score on admission), atrial fibrillation and HR on admission predicted its occurrence. HR <45 bpm occurred in 12 patients (5%) and was predicted by lower HR on admission. Neither HR ≥120 nor HR <45 bpm independently predicted poor outcome at three moths. Stroke location had no effect on the occurrence of HR violations. Clinical severity and age remained the only consistent predictors of poor outcome.</p> <p>Conclusions</p> <p>Significant tachycardia and bradycardia are frequent phenomena in acute stroke; however they do not independently predict clinical course or outcome. Continuous monitoring allows detecting rhythm disturbances in stroke patients and allows deciding whether urgent medical treatment is necessary.</p

Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening

Cell-based high content screening (HCS) is becoming an important and increasingly favored approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology. Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.National Institutes of Health (U.S.) (Grant