Fitzpatrickhesselberg4155

The rising incidence of antibiotic-resistant lung infections has instigated a much-needed search for new therapeutic strategies. One proposed strategy is the use of exogenous surfactants to deliver antimicrobial peptides (AMPs), like CATH-2, to infected regions of the lung. CATH-2 can kill bacteria through a diverse range of antibacterial pathways and exogenous surfactant can improve pulmonary drug distribution. Unfortunately, mixing AMPs with commercially available exogenous surfactants has been shown to negatively impact their antimicrobial function. It was hypothesized that the phosphatidylglycerol component of surfactant was inhibiting AMP function and that an exogenous surfactant, with a reduced phosphatidylglycerol composition would increase peptide mediated killing at a distal site. To better understand how surfactant lipids interacted with CATH-2 and affected its function, isothermal titration calorimetry and solid-state nuclear magnetic resonance spectroscopy as well as bacterial killing curves against Pseudomonas aeruginosa were utilized. Additionally, the wet bridge transfer system was used to evaluate surfactant spreading and peptide transport. Phosphatidylglycerol was the only surfactant lipid to significantly inhibit CATH-2 function, showing a stronger electrostatic interaction with the peptide than other lipids. Although diluting the phosphatidylglycerol content in an existing surfactant, through the addition of other lipids, significantly improved peptide function and distal killing, it also reduced surfactant spreading. A synthetic phosphatidylglycerol-free surfactant however, was shown to further improve CATH-2 delivery and function at a remote site. Based on these in vitro experiments synthetic phosphatidylglycerol-free surfactants seem optimal for delivering AMPs to the lung.Diatoms are an ecologically fundamental and highly diverse group of algae, dominating marine primary production in both open-water and coastal communities. The diatoms include both centric species, which may have radial or polar symmetry, and the pennates, which include raphid and araphid species and arose within the centric lineage. Here, we use combined microscopic and molecular information to reclassify a diatom strain CCMP470, previously annotated as a radial centric species related to Leptocylindrus danicus, as an araphid pennate species in the staurosiroid lineage, within the genus Plagiostriata. CCMP470 shares key ultrastructural features with Plagiostriata taxa, such as the presence of a sternum with parallel striae, and the presence of a highly reduced labiate process on its valve; and this evolutionary position is robustly supported by multigene phylogenetic analysis. We additionally present a draft genome of CCMP470, which is the first genome available for a staurosiroid lineage. 270 Pfams (19%) found in the CCMP470 genome are not known in other diatom genomes, which otherwise does not hold big novelties compared to genomes of non-staurosiroid diatoms. Notably, our DNA library contains the genome of a bacterium within the Rhodobacterales, an alpha-proteobacterial lineage known frequently to associate with algae. We demonstrate the presence of commensal alpha-proteobacterial sequences in other published algal genome and transcriptome datasets, which may indicate widespread and persistent co-occurrence.Grain refinement has been a topic of extensive research due to its scientific and technological importance as a common industrial practice for over seven decades. The traditional approach to grain refinement has been to reduce nucleation undercooling by the addition of potent nucleant particles. Here we show both theoretically and experimentally that more significant grain refinement can be achieved through increasing nucleation undercooling by using impotent nucleant particles. Based on the concept of explosive grain initiation, this new approach is illustrated by grain initiation maps and grain refinement maps and validated by experiments. It is anticipated that this new approach may lead to a profound change in both nucleation research and industrial practice well beyond metal casting.Deep neural networks can extract clinical information, such as diabetic retinopathy status and individual characteristics (e.g. age and sex), from retinal images. Here, we report the first study to train deep learning models with retinal images from 3,000 Qatari citizens participating in the Qatar Biobank study. We investigated whether fundus images can predict cardiometabolic risk factors, such as age, sex, blood pressure, smoking status, glycaemic status, total lipid panel, sex steroid hormones and bioimpedance measurements. Additionally, the role of age and sex as mediating factors when predicting cardiometabolic risk factors from fundus images was studied. Predictions at person-level were made by combining information of an optic disc centred and a macula centred image of both eyes with deep learning models using the MobileNet-V2 architecture. An accurate prediction was obtained for age (mean absolute error (MAE) 2.78 years) and sex (area under the curve 0.97), while an acceptable performance was achieved for systolic blood pressure (MAE 8.96 mmHg), diastolic blood pressure (MAE 6.84 mmHg), Haemoglobin A1c (MAE 0.61%), relative fat mass (MAE 5.68 units) and testosterone (MAE 3.76 nmol/L). We discovered that age and sex were mediating factors when predicting cardiometabolic risk factors from fundus images. We have found that deep learning models indirectly predict sex when trained for testosterone. For blood pressure, Haemoglobin A1c and relative fat mass an influence of age and sex was observed. However, achieved performance cannot be fully explained by the influence of age and sex. In conclusion we confirm that age and sex can be predicted reliably from a fundus image and that unique information is stored in the retina that relates to blood pressure, Haemoglobin A1c and relative fat mass. SM-102 chemical Future research should focus on stratification when predicting person characteristics from a fundus image.Personalized cancer treatments using combinations of drugs with a synergistic effect is attractive but proves to be highly challenging. Here we present an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug effects. For this we used dose-response data from pharmacogenomic encyclopedias and represent these as a drug atlas. The drug atlas represents the relations between drug effects and allows to identify independent processes for which the tumor might be particularly vulnerable when attacked by two drugs. Our approach enables the prediction of combination-therapy which can be linked to tumor-driving mutations. By using this strategy, we can uncover potential effective drug combinations on a pan-cancer scale. Predicted synergies are provided and have been validated in glioblastoma, breast cancer, melanoma and leukemia mouse-models, resulting in therapeutic synergy in 75% of the tested models. This indicates that we can accurately predict effective drug combinations with translational value.