Positive mood on the day of influenza vaccination predicts vaccine effectiveness: A prospective observational cohort study.

Influenza vaccination is estimated to only be effective in 17-53% of older adults. Multiple patient behaviors and psychological factors have been shown to act as 'immune modulators' sufficient to influence vaccination outcomes. However, the relative importance of such factors is unknown as they have typically been examined in isolation. The objective of the present study was to explore the effects of multiple behavioral (physical activity, nutrition, sleep) and psychological influences (stress, positive mood, negative mood) on the effectiveness of the immune response to influenza vaccination in the elderly. A prospective, diary-based longitudinal observational cohort study was conducted. One hundred and thirty-eight community-dwelling older adults (65-85years) who received the 2014/15 influenza vaccination completed repeated psycho-behavioral measures over the two weeks prior, and four weeks following influenza vaccination. IgG responses to vaccination were measured via antigen microarray and seroprotection via hemagglutination inhibition assays at 4 and 16weeks post-vaccination. High pre-vaccination seroprotection levels were observed for H3N2 and B viral strains. Positive mood on the day of vaccination was a significant predictor of H1N1 seroprotection at 16weeks post-vaccination and IgG responses to vaccination at 4 and 16weeks post-vaccination, controlling for age and gender. Positive mood across the 6-week observation period was also significantly associated with post-vaccination H1N1 seroprotection and IgG responses to vaccination at 16weeks post-vaccination, but in regression models the proportion of variance explained was lower than for positive mood on the day of vaccination alone. No other factors were found to significantly predict antibody responses to vaccination. Greater positive mood in older adults, particularly on the day of vaccination, is associated with enhanced responses to vaccination.

A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing.

TNF receptor associated periodic syndrome (TRAPS) is an autoinflammatory disease caused by mutations in TNF Receptor 1 (TNFR1). Current therapies for TRAPS are limited and do not target the pro-inflammatory signalling pathways that are central to the disease mechanism. Our aim was to identify drugs for repurposing as anti-inflammatories based on their ability to down-regulate molecules associated with inflammatory signalling pathways that are activated in TRAPS. This was achieved using rigorously optimized, high through-put cell culture and reverse phase protein microarray systems to screen compounds for their effects on the TRAPS-associated inflammatory signalome. 1360 approved, publically available, pharmacologically active substances were investigated for their effects on 40 signalling molecules associated with pro-inflammatory signalling pathways that are constitutively upregulated in TRAPS. The drugs were screened at four 10-fold concentrations on cell lines expressing both wild-type (WT) TNFR1 and TRAPS-associated C33Y mutant TNFR1, or WT TNFR1 alone; signalling molecule levels were then determined in cell lysates by the reverse-phase protein microarray. A novel mathematical methodology was developed to rank the compounds for their ability to reduce the expression of signalling molecules in the C33Y-TNFR1 transfectants towards the level seen in the WT-TNFR1 transfectants. Seven high-ranking drugs were selected and tested by RPPA for effects on the same 40 signalling molecules in lysates of peripheral blood mononuclear cells (PBMCs) from C33Y-TRAPS patients compared to PBMCs from normal controls. The fluoroquinolone antibiotic lomefloxacin, as well as others from this class of compounds, showed the most significant effects on multiple pro-inflammatory signalling pathways that are constitutively activated in TRAPS; lomefloxacin dose-dependently significantly reduced expression of 7/40 signalling molecules across the Jak/Stat, MAPK, NF-κB and PI3K/AKT pathways. This study demonstrates the power of signalome screening for identifying candidates for drug repurposing.

End-to-End Fovea Localisation in Colour Fundus Images With a Hierarchical Deep Regression Network.

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components.

Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the deep learning model is computationally expensive, while resizing the original image to achieve low resolution incurs information loss. Some hard-attention based approaches have emerged to select possible lesion regions from images to avoid processing the original image. However, these hard-attention based approaches usually take a long time to converge with weak guidance, and valueless patches may be trained by the classifier. To overcome this problem, we propose a deep selective attention approach that aims to select valuable regions in the original images for classification. In our approach, a decision network is developed to decide where to crop and whether the cropped patch is necessary for classification. These selected patches are then trained by the classification network, which then provides feedback to the decision network to update its selection policy. With such a co-evolution training strategy, we show that our approach can achieve a fast convergence rate and high classification accuracy. Our approach is evaluated on a public breast cancer histopathological image database, where it demonstrates superior performance compared to state-of-the-art deep learning approaches, achieving approximately 98% classification accuracy while only taking 50% of the training time of the previous hard-attention approach.

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system, which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumor and non-tumor), a second FCN to extract tumor nuclei region, and a multi-column convolutional neural network, which takes the outputs of the first two FCNs and the stain intensity description image as an input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as the input and directly outputs a clinical score. We will present experimental results, which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.