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Background: Although estimates of suboptimal adherence to oral corticosteroids in asthma range from 30% to 50%, no ideal method for measurement exists; the impact of poor adherence in severe asthma is likely to be particularly high. Research Questions: What is the prevalence of suboptimal adherence detected by self-reporting and direct measures? Is suboptimal adherence associated with disease activity? Study Design and Methods: Data were included from individuals with severe asthma taking part in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study and prescribed daily oral corticosteroids. Participants completed the Medication Adherence Report Scale, a five-item questionnaire used to grade adherence on a scale from 1 to 5, and provided a urine sample for analysis of prednisolone and metabolites by liquid chromatography-mass spectrometry. Results: Data from 166 participants were included in this study: mean (SD) age, 54.2 (± 11.9) years; FEV 1, 65.1% (± 20.5%) predicted; female, 58%; 37% completing the Medication Adherence Report Scale reported suboptimal adherence; and 43% with urinary corticosteroid data did not have detectable prednisolone or metabolites in their urine. Good adherence by both methods was detected in 49 of the 142 (35%) of participants in whom both methods were performed; adherence detection did not match between methods in 53%. Self-reported high adherers had better asthma control and quality of life, whereas directly measured high adherers had lower blood eosinophil levels. Interpretation: Low adherence is a common problem in severe asthma, whether measured directly or self-reported. We report poor agreement between the two methods, suggesting some disassociation between self-assessment of medication adherence and regular oral corticosteroid use, which suggests that each approach may provide complementary information in clinical practice.

Background Patients with severe asthma may have a greater risk of dying from COVID-19 disease. Angiotensin converting enzyme-2 (ACE2) and the enzyme proteases, transmembrane protease serine 2 (TMPRSS2) and FURIN, are needed for viral attachment and invasion into host cells. Methods We examined microarray mRNA expression of ACE2, TMPRSS2 and FURIN in sputum, bronchial brushing and bronchial biopsies of the European U-BIOPRED cohort. Clinical parameters and molecular phenotypes, including asthma severity, sputum inflammatory cells, lung functions, oral corticosteroid (OCS) use, and transcriptomic-associated clusters, were examined in relation to gene expression levels. Results ACE2 levels were significantly increased in sputum of severe asthma compared to mild-moderate asthma. In multivariate analyses, sputum ACE2 levels were positively associated with OCS use and male gender. Sputum FURIN levels were significantly related to neutrophils (%) and the presence of severe asthma. In bronchial brushing samples, TMPRSS2 levels were positively associated with male gender and body mass index, whereas FURIN levels with male gender and blood neutrophils. In bronchial biopsies, TMPRSS2 levels were positively related to blood neutrophils. The neutrophilic molecular phenotype characterised by high inflammasome activation expressed significantly higher FURIN levels in sputum than the eosinophilic Type 2-high or the pauci-granulocytic oxidative phosphorylation phenotypes. Conclusion Levels of ACE2 and FURIN may differ by clinical or molecular phenotypes of asthma. Sputum FURIN expression levels were strongly associated with neutrophilic inflammation and with inflammasome activation. This might indicate the potential for a greater morbidity and mortality outcome from SARS-CoV-2 infection in neutrophilic severe asthma.

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart-brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

BACKGROUND: Macrophages control innate and acquired immunity but their role in severe asthma remains ill-defined. We investigated gene signatures of macrophage subtypes in the sputum of 104 asthmatics and 16 healthy volunteers from the U-BIOPRED cohort. METHODS: Forty-nine gene signatures (modules) for differentially stimulated macrophages, one to assess lung tissue-resident cells (TR-Mφ) and two for their polarization (classically- and alternatively-activated macrophages: M1 and M2 respectively) were studied using gene set variation analysis. We calculated enrichment scores (ES) across severity and previously identified asthma transcriptome-associated clusters (TACs). RESULTS: Macrophage numbers were significantly decreased in severe asthma compared to mild-moderate asthma and healthy volunteers. The ES for most modules were also significantly reduced in severe asthma except for 3 associated with inflammatory responses driven by TNF and Toll-like receptors via NF-κB, eicosanoid biosynthesis via the lipoxygenase pathway and IL-2 biosynthesis (all p

The recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. One week after initial symptoms develop, a subset of patients progresses to severe disease, with high mortality and limited treatment options. To design novel interventions aimed at preventing spread of the virus and reducing progression to severe disease, detailed knowledge of the cell types and regulating factors driving cellular entry is urgently needed. Here we assess the expression patterns in genes required for COVID-19 entry into cells and replication, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi). Matched samples from the upper and lower airways show a clear increased expression of these genes in the nose compared to the bronchi and parenchyma. Cellular deconvolution indicates a clear association of these genes with the proportion of secretory epithelial cells. Smoking status was found to increase the majority of COVID-19 related genes including ACE2 and TMPRSS2 but only in the lower airways, which was associated with a significant increase in the predicted proportion of goblet cells in bronchial samples of current smokers. Both acute and second hand smoke were found to increase ACE2 expression in the bronchus. Inhaled corticosteroids decrease ACE2 expression in the lower airways. No significant effect of genetics on ACE2 expression was observed, but a strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified in the bronchus.

Background: Transcriptomic changes in patients who respond clinically to biological therapies may identify responses in other tissues or diseases. Objective: We sought to determine whether a disease signature identified in atopic dermatitis (AD) is seen in adults with severe asthma and whether a transcriptomic signature for patients with AD who respond clinically to anti–IL-22 (fezakinumab [FZ]) is enriched in severe asthma. Methods: An AD disease signature was obtained from analysis of differentially expressed genes between AD lesional and nonlesional skin biopsies. Differentially expressed genes from lesional skin from therapeutic superresponders before and after 12 weeks of FZ treatment defined the FZ-response signature. Gene set variation analysis was used to produce enrichment scores of AD and FZ-response signatures in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes asthma cohort. Results: The AD disease signature (112 upregulated genes) encompassing inflammatory, T-cell, T H2, and T H17/T H22 pathways was enriched in the blood and sputum of patients with asthma with increasing severity. Patients with asthma with sputum neutrophilia and mixed granulocyte phenotypes were the most enriched (P H22/IL-22 pathways. Conclusions: The FZ-response signature in AD identifies severe neutrophilic asthmatic patients as potential responders to FZ therapy. This approach will help identify patients for future asthma clinical trials of drugs used successfully in other chronic diseases.

Background Approximately 3%-5% of lung adenocarcinoma is driven by anaplastic lymphoma kinase (ALK) fusion oncogene, whose activity can be suppressed by multiple ALK inhibitors. Crizotinib and ceritinib have demonstrated superior efficacy to platinum-based chemotherapy as front-line treatment for patients with ALK-positive advanced non-small cell lung cancer (NSCLC). However, the direct comparison between them in the front-line setting remains lacking. Methods A total of 48 patients with ALK-positive, previously untreated advanced NSCLC, who received crizotinib and ceritinib as front-line treatment were retrospectively investigated. The efficacy and pattern of disease progression were analyzed. Results Patients receiving ceritinib treatment were significantly younger than those receiving crizotinib treatment (52.0 vs. 63.0, P = 0.016). The median progression-free survival (PFS) was significantly longer with ceritinib than with crizotinib treatment (32.3 vs. 12.9 months; log-rank P = 0.020); the hazard ratio for disease progression or death, 0.27 (95% CI, 0.08-0.90; P = 0.033). An objective response was noted in all patients in the ceritinib group and in 23 patients in the crizotinib group (74.2%; 95% CI, 59.0 to 88.5). The rate of systemic progression was significantly lower over time with ceritinib treatment compared to crizotinib treatment (cause-specific hazard ratio, 0.21; 95% CI 0.06-0.73; P = 0.014). Serious adverse events were noted in one (2.9%) patient showing elevated liver function in the crizotinib group and three (23.1%) patients showing diarrhea in the ceritinib group. Dose reduction was needed in five out of 13 (38.5%) patients receiving ceritinib treatment. Conclusion Ceritinib showed higher efficacy associated with a better control of systemic progression compared to crizotinib for the front-line treatment of ALK-positive advanced NSCLCs.

Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as it involves processing tens of teravoxels of imaging data. Validating such reconstructions is extremely laborious. We develop TeraVR, an open-source virtual reality annotation system, to address these challenges. TeraVR integrates immersive and collaborative 3-D visualization, interaction, and hierarchical streaming of teravoxel-scale images. Using TeraVR, we have produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction., Reconstructing the full shape of neurons is a major informatics challenge as it requires handling huge whole-brain imaging datasets. Here the authors present an open-source virtual reality annotation system for precise and efficient data production of neuronal shapes reconstructed from whole brains.

BACKGROUND Single agent immune checkpoint inhibitors (ICIs) improve survival outcomes compared to chemotherapy for advanced non-small cell lung cancer (NSCLC), but treatment efficacy widely varies. The combination of ICIs with chemotherapy has shown promising efficacy over chemotherapy alone; however, whether this strategy is superior to single agent ICIs for the treatment of advanced NSCLC remains unknown. METHODS The records of 109 patients with advanced NSCLC who were administered at least one cycle of ICIs were retrospectively reviewed. Patients were grouped based on the presence or absence of a chemotherapy treatment combination. Efficacy and survival outcomes were analyzed. RESULT Sixty-nine (58.0%) patients received single agent ICIs (ICI group) and 50 (42.0%) received ICIs and chemotherapy (ICC group). The median (3.2 vs. 3.0 months; P = 0.025) and one-year (34.5 vs. 9.6%; P = 0.026) progression-free survival (PFS) rates were significantly better in the ICC than in the ICI group. The superior efficacy of ICC remained in the propensity score matched pairs (median PFS 3.2 vs. 2.6 months, P = 0.032; 1-year PFS 35.2 vs. 7.6%; P = 0.035). Eastern Cooperative Oncology Group performance status 0-1 (HR 0.37, 95% CI 0.22-0.62; P < 0.001) and the ICC group (HR 0.56, 95% CI 0.34-0.94; P = 0.028) were predictive of PFS. Subgroup-to-chemotherapy interaction revealed improved risk reduction for adenocarcinoma and EGFR mutation. CONCLUSION Combing chemotherapy with ICIs improved treatment efficacy over ICIs alone. The additional efficacy of chemotherapy may differ between histological subtypes and EGFR mutation status.

Background: There are limited comparisons of first- and second-generation EGFR tyrosine kinase inhibitors (TKIs) in large, real-world cohorts of non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor ( EGFR) mutations. Methods: Patients with advanced NSCLC ( N = 612) with common EGFR mutations receiving first-line gefitinib/erlotinib and afatinib were grouped and propensity-score matched. Progression-free survival (PFS), overall survival (OS) and secondary T790M mutations were analyzed. Results: The gefitinib/erlotinib and afatinib groups each contained 206 patients after matching. Compared with gefitinib/erlotinib, patients receiving afatinib achieved longer median PFS (16.3 versus 14.2 months; log-rank test p = 0.020) and had a lower risk of progression [hazard ratio (HR) 0.73 (95% confidence interval (CI), 0.57–0.94); p = 0.017]. Median OS (37.3 versus 34.2 months; log-rank test p = 0.500) and reduction in risk of death [HR 0.89 (95% CI, 0.65–1.23); p = 0.476] did not differ significantly between groups. T790M positivity was significantly higher in the gefitinib/erlotinib than afatinib group (70.9% versus 44.6%, p Conclusion: Compared with gefitinib/erlotinib, afatinib had a higher treatment efficacy and a lower secondary T790M positivity in a large, real-world cohort of Asian patients with EGFR-mutated NSCLC.

Transposable elements (TEs) are widespread across eukaryotic genomes, yet their content varies widely between different species. Factors shaping the diversity of TEs are poorly understood. Understanding the evolution of TEs is difficult because their sequences diversify rapidly and TEs are often transferred through non‐conventional means such as horizontal gene transfer. We developed a method to track TE evolution using network analysis to visualise TE sequence and TE content across different genomes. We illustrate our method by first using a monopartite network to study the sequence evolution of Tc1/mariner elements across focal species. We identify a connection between two subfamilies associated with convergent acquisition of a domain from a protein‐coding gene. Second, we use a bipartite network to study how TE content across species is shaped by epigenetic silencing mechanisms. We show that the presence of Piwi‐interacting RNAs is associated with differences in network topology after controlling for phylogenetic effects. Together, our method demonstrates how a network‐based approach can identify hitherto unknown properties of TE evolution across species., A novel network‐based method is used to study transposable element (TE) evolution, revealing new insights into how epigenetic silencing mechanisms affect TE content across species.

High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed support multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy comparing to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various application of high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making., 14 pages, 8 figures, 7 tables

Background Recent studies have revealed that many discharged patients with COVID-19 experience ongoing symptoms months later. Rehabilitation interventions can help address the consequences of COVID-19, including medical, physical, cognitive, and psychological problems. To our knowledge, no studies have investigated the effects of rehabilitation following discharge from hospital for patients with COVID-19. Objective The specific aims of this project are to investigate the effects of a 12-week exercise program on pulmonary fibrosis in patients recovering from COVID-19. A further aim will be to examine how Chinese herbal medicines as well as the gut microbiome and its metabolites regulate immune function and possibly autoimmune deficiency in the rehabilitation process. Methods In this triple-blinded, randomized, parallel-group, controlled clinical trial, we will recruit adult patients with COVID-19 who have been discharged from hospital in Hong Kong and are experiencing impaired lung function and pulmonary function. A total of 172 eligible patients will be randomized into four equal groups: (1) cardiorespiratory exercise plus Chinese herbal medicines group, (2) cardiorespiratory exercise only group, (3) Chinese herbal medicines only group, and (4) waiting list group (in which participants will receive Chinese herbal medicines after 24 weeks). These treatments will be administered for 12 weeks, with a 12-week follow-up period. Primary outcomes include dyspnea, fatigue, lung function, pulmonary function, blood oxygen levels, immune function, blood coagulation, and related blood biochemistry. Measurements will be recorded prior to initiating the above treatments and repeated at the 13th and 25th weeks of the study. The primary analysis is aimed at comparing the outcomes between groups throughout the study period with an α level of .05 (two-tailed). Results The trial has been approved by the university ethics committee following the Declaration of Helsinki (approval number: REC/19-20/0504) in 2020. The trial has been recruiting patients. The data collection will be completed in 24 months, from January 1, 2021, to December 31, 2022. Conclusions Given that COVID-19 and its sequelae would persist in human populations, important findings from this study would provide valuable insights into the mechanisms and processes of COVID-19 rehabilitation. Trial Registration ClinicalTrials.gov NCT04572360; https://clinicaltrials.gov/ct2/show/NCT04572360 International Registered Report Identifier (IRRID) PRR1-10.2196/25556

Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the existing models, however, have been overengineered to consist of many layers or have introduced additional steps in the processing pipeline, such as converting signals to spectrogram-based images. They require to be trained on a large dataset to prevent the overfitting problem (but most of the sleep datasets contain a limited amount of class-imbalanced data) and are difficult to be applied (as there are many hyperparameters to be configured in the pipeline). In this paper, we propose an efficient deep learning model, named TinySleepNet, and a novel technique to effectively train the model end-to-end for automatic sleep stage scoring based on raw single-channel EEG. Our model consists of a less number of model parameters to be trained compared to the existing ones, requiring a less amount of training data and computational resources. Our training technique incorporates data augmentation that can make our model be more robust the shift along the time axis, and can prevent the model from remembering the sequence of sleep stages. We evaluated our model on seven public sleep datasets that have different characteristics in terms of scoring criteria and recording channels and environments. The results show that, with the same model architecture and the training parameters, our method achieves a similar (or better) performance compared to the state-of-the-art methods on all datasets. This demonstrates that our method can generalize well to the largest number of different datasets.

BackgroundThe recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. A subset of COVID-19 patients progresses to severe disease, with high mortality and limited treatment options. Detailed knowledge of the expression regulation of genes required for viral entry into respiratory epithelial cells is urgently needed.MethodsHere we assess the expression patterns of genes required for SARS-CoV-2 entry into cells, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi).FindingsGenes encoding viral receptors and activating protease are increased in the nose compared to the bronchi in matched samples and associated with the proportion of secretory epithelial cells in cellular deconvolution analyses. Current or ex-smoking was found to increase expression of these genes only in lower airways, which was associated with a significant increase in the predicted proportion of goblet cells. Both acute and second hand smoke exposure were found to increase ACE2 expression while inhaled corticosteroids decrease ACE2 expression in the lower airways. A strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified.InterpretationGenes associated with SARS-CoV-2 viral entry into cells are high in upper airways, but strongly increased in lower airways by smoke exposure. In contrast, ICS decreases ACE2 expression, indicating that inhaled corticosteroids are unlikely to increase the risk for more severe COVID-19 disease.FundingThis work was supported by a Seed Network grant from the Chan Zuckerberg Initiative to M.C.N. and by the European Union’s H2020 Research and Innovation Program under grant agreement no. 874656 (discovAIR) to M.C.N. U BIOPRED was supported by an Innovative Medicines Initiative Joint Undertaking (No. 115010), resources from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution (www.imi.europa.eu). Longfonds Junior Fellowship. We acknowledge the contribution of the whole U-BIOPRED team as listed in the Appendix S1.’ SDB, FM and RFS would like to thank the Helmholtz Association, Germany, for support.” NIH K08HL146943; Parker B. Francis Fellowship; ATS Foundation/Boehringer Ingelheim Pharmaceuticals Inc. Research Fellowship in IPF. RCR is part funded by Cancer Research UK Cambridge Centre and the Cambridge NIHR Biomedical Research Centre. BAP was funded by programme support from Cancer Research UK. The CRUKPAP Study was supported by the CRUK Cambridge Cancer Centre, by the NIHR Cambridge Biomedical Research Centre and by the Cambridge Bioresource. PIAMA was supported by The Netherlands Organization for Health Research and Development; The Netherlands Organization for Scientific Research; The Netherlands Lung Foundation (with methylation studies supported by AF 4.1.14.001); The Netherlands Ministry of Spatial Planning, Housing, and the Environment; and The Netherlands Ministry of Health, Welfare, and Sport. Dr. Qi is supported by a grant from the China Scholarship Council.

In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model is deployed into the real clinical world, the model may not perform optimally. A major challenge is the potential poor-quality segmentations generated due to degraded image quality or domain shift issues. There is a timely need to develop an automated quality control method that can detect poor segmentations and feedback to clinicians. Here we propose a novel deep generative model-based framework for quality control of cardiac MRI segmentation. It first learns a manifold of good-quality image-segmentation pairs using a generative model. The quality of a given test segmentation is then assessed by evaluating the difference from its projection onto the good-quality manifold. In particular, the projection is refined through iterative search in the latent space. The proposed method achieves high prediction accuracy on two publicly available cardiac MRI datasets. Moreover, it shows better generalisation ability than traditional regression-based methods. Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows., Comment: The paper is accepted to MICCAI 2020

RATIONALE: Vitamin D deficiency is common in patients with asthma and COPD. Low 25-hydroxyvitamin D (25[OH]D) levels may represent a cause or a consequence of these conditions. OBJECTIVE: To determine whether vitamin D metabolism is altered in asthma or COPD. METHODS: We conducted a longitudinal study in 186 adults to determine whether the 25(OH)D response to six oral doses of 3 mg vitamin D3, administered over one year, differed between those with asthma or COPD vs. controls. Serum concentrations of vitamin D3, 25(OH)D3 and 1α,25-dihydroxyvitamin D3 (1α,25[OH]2D3) were determined pre- and post-supplementation in 93 adults with asthma, COPD or neither condition, and metabolite-to-parent compound molar ratios were compared between groups to estimate hydroxylase activity. Additionally, we analyzed fourteen datasets to compare expression of 1α,25[OH]2D3-inducible gene expression signatures in clinical samples taken from adults with asthma or COPD vs. controls. MEASUREMENTS AND MAIN RESULTS: The mean post-supplementation 25(OH)D increase in participants with asthma (20.9 nmol/L) and COPD (21.5 nmol/L) was lower than in controls (39.8 nmol/L; P=0.001). Compared with controls, patients with asthma and COPD had lower molar ratios of 25(OH)D3-to-vitamin D3 and higher molar ratios of 1α,25(OH)2D3-to-25(OH)D3 both pre- and post-supplementation (P≤0.005). Inter-group differences in 1α,25[OH]2D3-inducible gene expression signatures were modest and variable where statistically significant. CONCLUSIONS: Attenuation of the 25(OH)D response to vitamin D supplementation in asthma and COPD associated with reduced molar ratios of 25(OH)D3-to-vitamin D3 and increased molar ratios of 1α,25(OH)2D3-to-25(OH)D3 in serum, suggesting that vitamin D metabolism is dysregulated in these conditions.

Hyocholic acid (HCA) is a major bile acid (BA) species in the BA pool of pigs, a species known for its exceptional resistance to spontaneous development of diabetic phenotypes. HCA and its derivatives are also present in human blood and urine. We investigate whether human HCA profiles can predict the development of metabolic disorders. We find in the first cohort (n = 1107) that both obesity and diabetes are associated with lower serum concentrations of HCA species. A separate cohort study (n = 91) validates this finding and further reveals that individuals with pre-diabetes are associated with lower levels of HCA species in feces. Serum HCA levels increase in the patients after gastric bypass surgery (n = 38) and can predict the remission of diabetes two years after surgery. The results are replicated in two independent, prospective cohorts (n = 132 and n = 207), where serum HCA species are found to be strong predictors for metabolic disorders in 5 and 10 years, respectively. These findings underscore the association of HCA species with diabetes, and demonstrate the feasibility of using HCA profiles to assess the future risk of developing metabolic abnormalities., The early identification of metabolic disorders could improve or prevent overt disease. Here the authors show that the circulating concentration of hyocholic acid (HCA) species is decreased in the context of obesity and diabetes and increased after gastric bypass surgery in humans, and further that serum HCA species are predictive of metabolic outcomes in healthy individuals.

BACKGROUND VEGF plays a key role in tumor angiogenesis and immunosuppression. VEGF-blocking has proven beneficial for EGFR mutant and wild-type nonsquamous non-small cell lung cancer (nonsq-NSCLC); however, the number of cycles and treatment line yielding the optimal benefit are unknown. METHODS We retrospectively analyzed the data of 115 patients with advanced/metastatic nonsq-NSCLC administered at least one cycle of bevacizumab. The number of bevacizumab cycles was treated as a time-dependent covariate. Predictors of overall survival (OS) were investigated. RESULTS Bevacizumab was used as first-line treatment in 47 (40.9%) patients, with a median of five cycles (range: 1-31). Eastern Cooperative Oncology Group performance status ≥ 2 (hazard ratio [HR] 4.78, 95% confidence interval [CI] 2.68-8.51; P < 0.001), wild-type EGFR (HR 2.61, 95% CI 1.45-4.70; P = 0.001), and bleeding during bevacizumab treatment (HR 3.63, 95% CI 1.77-7.45; P < 0.001) were predictive of poor OS; the number of bevacizumab cycles and first-line administration were not. In the wild-type EGFR subgroup, the number of bevacizumab cycles (≥ 5 vs. 1-4) was associated with a significant OS benefit (HR 0.28, 95% CI 0.08-0.98; P = 0.044); first-line administration also showed an OS benefit (HR 0.48, 95% CI 0.20-1.17; P = 0.105). A significant association between the number of cycles and EGFR status was identified (P = 0.046). CONCLUSION OS benefit is negatively affected by bleeding events in bevacizumab-treated patients. Prolonged and early introduction of bevacizumab may provide an OS benefit for patients with wild-type EGFR nonsq-NSCLC.

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

BACKGROUND: Sputum analysis in asthmatic patients is used to define airway inflammatory processes and might guide therapy. OBJECTIVE: We sought to determine differential gene and protein expression in sputum samples from patients with severe asthma (SA) compared with nonsmoking patients with mild/moderate asthma. METHODS: Induced sputum was obtained from nonsmoking patients with SA, smokers/ex-smokers with severe asthma, nonsmoking patients with mild/moderate asthma (MMAs), and healthy nonsmoking control subjects. Differential cell counts, microarray analysis of cell pellets, and SOMAscan analysis of sputum analytes were performed. CRID3 was used to inhibit the inflammasome in a mouse model of SA. RESULTS: Eosinophilic and mixed neutrophilic/eosinophilic inflammation were more prevalent in patients with SA compared with MMAs. Forty-two genes probes were upregulated (>2-fold) in nonsmoking patients with severe asthma compared with MMAs, including IL-1 receptor (IL-1R) family and nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain containing 3 (NRLP3) inflammasome members (false discovery rate

Assessment of small bowel's motility plays an important role in the diagnosis of small bowel disease. Conventional assessment methods rely on hand-designed features or manual guide-line drawing, which results in difficult modeling and high time consumption, thus they are still inefficient and impractical for clinical uses. With the help of deep neural networks, we introduced a semi-automated approach, replacing hand-designed features with automatic feature extraction, and an automated approach, eliminating manual guide-line drawing, to assess small bowel motility by automatically marking cross-sectional diameters on small bowel images, measuring temporal fluctuation of diameter lengths, and evaluating contraction frequency. Experiment results show that proposed methods could estimate small bowel contraction frequency correctly. The difference between predicted diameter lengths and one manually labeled is within reasonable range, and estimated frequency is close to the groundtruth. Therefore, proposed methods can be utilized for diagnosis of small bowel disease, which will assist radiologist in decision-making.

Background Treatment for stage III non-small cell lung cancer (NSCLC) of unresectable disease mainly involves concurrent chemoradiation (CRT). Post-CRT consolidation treatment with durvalumab is a major therapeutic advance that provides survival benefit in this group of patients. However, the performance of this treatment strategy remains to be studied in a real-world setting. Methods A total of 31 patients who had disease control post-CRT were included in the durvalumab early access program (EAP) as an intent-to-treat cohort and retrospectively reviewed for post-CRT progression-free survival (PFS) and time to metastatic disease or death (TMDD). The neutrophil-to-lymphocyte ratio (NLR) at the initiation of durvalumab was analyzed in 29 patients. Results The median time from the completion of concurrent CRT to the initiation of durvalumb was 2.8 months. The objective response was 25.8% and the 12 month PFS and TMDD-free rate were 56.4% and 66.9%, respectively. The low NLR patients showed a significantly longer post-CRT PFS (not reach vs. 12.0 months [95% CI: 5.5-not estimable]; P = 0.040; the hazard ratio for disease progression or death, 0.23 [95% CI: 0.05-1.00]; P = 0.048) and the 12 month post-CRT PFS rate (82.5 vs. 42.6%). The post-CRT TMDD (not reach vs. 12.6 months, [95% CI: 10.8-not estimable]; P = 0.010; the hazard ratio for distant metastasis or death, 0.11 [95% CI: 0.01-0.88]; P = 0.037) and 12 month post-CRT TMDD-free rate (90.9 vs. 57.1%) were also significantly higher in the low NLR patients. Conclusions Durvalumab consolidation treatment in real-world patients showed substantial efficacy and the correlation with the NLR level warrants further investigation.

RationaleSevere asthma and COPD share common pathophysiologic traits such as relative corticosteroid insensitivity. We recently published three transcriptome-associated clusters (TACs) using hierarchical analysis of the sputum transcriptome in asthmatics from the U-BIOPRED cohort with one Type 2-high signature (TAC1) and 2 Type 2-low signatures.ObjectiveWe examined whether gene-expression signatures obtained in asthma can be used to identify the subgroup of COPD patients with steroid sensitivity.MethodsUsing gene set variation analysis (GSVA), we examined the distribution and enrichment scores (ES) of the 3 TACs in the transcriptome of bronchial biopsies from 46 patients who participated in the GLUCOLD COPD study that received 30 months of treatment with inhaled corticosteroids (ICS) with and without an added long-acting β-agonist (LABA). The identified signatures were then associated with longitudinal clinical variables after treatment.Measurements and main resultsBronchial biopsies in COPD patients at baseline showed a wide range of expression of the 3 TACs. After ICS±LABA treatment, the ES of TAC1 was significantly reduced at 30 months, but those of TAC2 and TAC3 were unaffected. A corticosteroid-sensitive TAC1 (sub)signature was developed from the TAC1 ICS-responsive genes. This signature consisted of mast cell-specific genes identified by single-cell RNA-seq and positively correlated with bronchial biopsy mast cell numbers following ICS±LABA. Baseline levels of gene transcription predicted change in FEV1 %predicted following 30-month ICS±LABA.ConclusionsSputum-derived transcriptomic signatures from an asthma cohort can be recapitulated in bronchial biopsies of COPD patients and identified airway wall mast cells as a predictor of those COPD patients who will benefit from inhaled corticosteroids.

Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven science, the success of machine learning, in particular supervised learning, largely depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire. It takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain tumour images that is able to suggest informative sample images for human experts to annotate. Our experiments show that training a segmentation model with only 19% suggestively annotated patient scans from BraTS 2019 dataset can achieve a comparable performance to training a model on the full dataset for whole tumour segmentation task. It demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications., Paper accepted by MICCAI 2020

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods., Accepted by BIBM 2019 (Regular)

Background Asthma prevalence is 339 million globally. ‘Severe asthma’ (SA) comprises subjects with uncontrolled asthma despite proper management. Objectives To compare asthma from diverse ethnicities and environments. Methods A cross-sectional analysis of two adult cohorts, a Brazilian (ProAR) and a European (U-BIOPRED). U-BIOPRED comprised of 311 non-smoking with Severe Asthma (SAn), 110 smokers or ex-smokers with SA (SAs) and 88 mild to moderate asthmatics (MMA) while ProAR included 544 SA and 452 MMA. Although these projects were independent, there were similarities in objectives and methodology, with ProAR adopting operating procedures of U-BIOPRED. Results Among SA subjects, age, weight, proportion of former smokers and FEV1 pre-bronchodilator were similar. The proportion of SA with a positive skin prick tests (SPT) to aeroallergens, the scores of sino-nasal symptoms and quality of life were comparable. In addition, blood eosinophil counts (EOS) and the % of subjects with EOS > 300 cells/μl were not different. The Europeans with SA however, were more severe with a greater proportion of continuous oral corticosteroids (OCS), worse symptoms and more frequent exacerbations. FEV1/FVC pre- and post-bronchodilator were lower among the Europeans. The MMA cohorts were less comparable in control and treatment, but similar in the proportion of allergic rhinitis, gastroesophageal reflux disease and EOS >3%. Conclusions ProAR and U-BIOPRED cohorts, with varying severity, ethnicity and environment have similarities, which provide the basis for global external validation of asthma phenotypes. This should stimulate collaboration between asthma consortia with the aim of understanding SA, which will lead to better management.

Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The "dimensionality curse" makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-to-end deep learning model called OmiVAE to extract low dimensional features and classify samples from multi-omics data. OmiVAE combines the basic structure of variational autoencoders with a classification network to achieve task-oriented feature extraction and multi-class classification. The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average classification accuracy of 97.49% after 10-fold cross-validation among 33 tumour types and normal samples, which shows better performance than other existing methods. The OmiVAE model learned from multi-omics data outperformed that using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification., 7 pages, 4 figures

Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the ‘manageability’ of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.

The first (1G) and second (2G) generations of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) show differential inhibitory capacities towards EGFR T790M-mutated non-small-cell lung cancer (NSCLC) cells. To assess the ratio of the allele fractions of T790M (AFT790M) to EGFR-activating mutations (AFmEGFR) in patients treated with 1G and 2G EGFR TKIs who acquired T790M-mediated resistance and to determine the relationship between AF and the later efficacy of osimertinib. The efficacy of osimertinib was reviewed for 54 T790M-positive EGFR-mutated NSCLC patients grouped by the generation of prior EGFR TKI use (1G vs. 2G). AFmEGFR and AFT790M were determined by QuantStudio digital PCR using tissues obtained upon acquired resistance. The progression-free survival (PFS; 20.3 vs. 11.6 months, p = 0.031) and the 1-year PFS rate (63.2 vs. 37.5%, p = 0.029) for osimertinib were significantly better for group 1G compared to group 2G. The ratio of AFT790M to AFmEGFR in group 1G was significantly higher than in group 2G (46.16 ± 5.40% vs. 25.86 ± 4.25%, p = 0.009). An unbiased analysis revealed three AF-associated clusters (ARCs) suggesting the ratio of AFT790M to AFmEGFR correlates with the efficacy of osimertinib. We found all patients in ARC2 having the highest ratio of AFT790M to AFmEGFR to have previously been treated with a 1G EGFR TKI and to show the longest osimertinib PFS compared to ARC3 (NR vs. 11.9 months, p = 0.060) and ARC1 (NR vs. 12.4 month, p = 0.045). Acquired T790M fraction of EGFR-mutated NSCLC is linked to different generations of prior EGFR TKI use and the later efficacy of osimertinib.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of morbidity and death globally. The lack of effective treatments results from an incomplete understanding of the underlying mechanisms driving COPD pathogenesis.Interleukin (IL)-22 has been implicated in airway inflammation and is increased in COPD patients. However, its roles in the pathogenesis of COPD is poorly understood. Here, we investigated the role of IL-22 in human COPD and in cigarette smoke (CS)-induced experimental COPD.IL-22 and IL-22 receptor mRNA expression and protein levels were increased in COPD patients compared to healthy smoking or non-smoking controls. IL-22 and IL-22 receptor levels were increased in the lungs of mice with experimental COPD compared to controls and the cellular source of IL-22 included CD4+ T-helper cells, γδ T-cells, natural killer T-cells and group 3 innate lymphoid cells. CS-induced pulmonary neutrophils were reduced in IL-22-deficient (Il22−/−) mice. CS-induced airway remodelling and emphysema-like alveolar enlargement did not occur in Il22−/− mice. Il22−/− mice had improved lung function in terms of airway resistance, total lung capacity, inspiratory capacity, forced vital capacity and compliance.These data highlight important roles for IL-22 and its receptors in human COPD and CS-induced experimental COPD.

Background For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. Results The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. Conclusions In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis. Electronic supplementary material The online version of this article (10.1186/s12859-019-2748-y) contains supplementary material, which is available to authorized users.

Today, healthcare providers are experiencing explosive growth in data. Although the dramatic increase in the use of medical imaging technologies has been a major contributor to healthcare data growth in the past decade, more recently the rising adoption of sensing devices, enabling people to collect health-related data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily generated and diverse health data.This paper explores the potential for sensors use in healthcare data acquisition and presents the next evolution in the on-going development of Wiki-Health, a big data service platform, designed to address the larger problem of explosive growth in healthcare information by providing a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, the platform is designed to allow users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users-including health professionals, patients or even individuals who desire to maintain an optimum level of personal health-on a massive scale.To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage model capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. The design of such a hybrid model allows Wiki-Health to potentially handle heterogeneous formats of sensor data. In addition to its data management capabilities, we envision the potential for Wiki-Health as a system that also enables health sensor data monitoring and analysis, not only as a method of tracking existing health conditions but also as a means of encouraging a more pro-active approach to healthcare through early detection. To tackle the scalability and performance challenges of real-time analysis, the Analysis Tasks Allocation Scheme proposed in the research aids the management of data analysis tasks on a large scale and utilises the elastic nature of cloud infrastructure by considering the aspects of performance and cost.To evaluate the proposed Wiki-Health approach, we have developed an ECG-based health monitoring service on top of the Wiki-Health platform. The positive performance of the approach is supported by the results obtained in our experimental trials and shows significant potential for real-world applications. Propose a hybrid storage approach for managing heterogeneous formats of sensor data.Wiki-Health provides a unified solution for managing personal health sensor data.Wiki-Health Analysis Framework allows users to reuse and remix data and models.Analysis Tasks Allocation Scheme (ATAS) targets application level performance metric.Present an efficient ECG-based health monitoring service on top of Wiki-Health.

BackgroundAlthough several studies link high levels of IL-6 and soluble IL-6 receptor (sIL-6R) to asthma severity and decreased lung function, the role of IL-6 trans-signaling (IL-6TS) in asthmatic patients is unclear.ObjectiveWe sought to explore the association between epithelial IL-6TS pathway activation and molecular and clinical phenotypes in asthmatic patients.MethodsAn IL-6TS gene signature obtained from air-liquid interface cultures of human bronchial epithelial cells stimulated with IL-6 and sIL-6R was used to stratify lung epithelial transcriptomic data (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes [U-BIOPRED] cohorts) by means of hierarchical clustering. IL-6TS–specific protein markers were used to stratify sputum biomarker data (Wessex cohort). Molecular phenotyping was based on transcriptional profiling of epithelial brushings, pathway analysis, and immunohistochemical analysis of bronchial biopsy specimens.ResultsActivation of IL-6TS in air-liquid interface cultures reduced epithelial integrity and induced a specific gene signature enriched in genes associated with airway remodeling. The IL-6TS signature identified a subset of patients with IL-6TS–high asthma with increased epithelial expression of IL-6TS–inducible genes in the absence of systemic inflammation. The IL-6TS–high subset had an overrepresentation of frequent exacerbators, blood eosinophilia, and submucosal infiltration of T cells and macrophages. In bronchial brushings Toll-like receptor pathway genes were upregulated, whereas expression of cell junction genes was reduced. Sputum sIL-6R and IL-6 levels correlated with sputum markers of remodeling and innate immune activation, in particular YKL-40, matrix metalloproteinase 3, macrophage inflammatory protein 1β, IL-8, and IL-1β.ConclusionsLocal lung epithelial IL-6TS activation in the absence of type 2 airway inflammation defines a novel subset of asthmatic patients and might drive airway inflammation and epithelial dysfunction in these patients.

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net., Comment: Accepted by MICCAI 2019

Background The role of IL-17 immunity is well established in patients with inflammatory diseases, such as psoriasis and inflammatory bowel disease, but not in asthmatic patients, in whom further study is required. Objective We sought to undertake a deep phenotyping study of asthmatic patients with upregulated IL-17 immunity. Methods Whole-genome transcriptomic analysis was performed by using epithelial brushings, bronchial biopsy specimens (91 asthmatic patients and 46 healthy control subjects), and whole blood samples (n = 498) from the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort. Gene signatures induced in vitro by IL-17 and IL-13 in bronchial epithelial cells were used to identify patients with IL-17–high and IL-13–high asthma phenotypes. Results Twenty-two of 91 patients were identified with IL-17, and 9 patients were identified with IL-13 gene signatures. The patients with IL-17–high asthma were characterized by risk of frequent exacerbations, airway (sputum and mucosal) neutrophilia, decreased lung microbiota diversity, and urinary biomarker evidence of activation of the thromboxane B2 pathway. In pathway analysis the differentially expressed genes in patients with IL-17-high asthma were shared with those reported as altered in psoriasis lesions and included genes regulating epithelial barrier function and defense mechanisms, such as IL1B, IL6, IL8, and β-defensin. Conclusion The IL-17–high asthma phenotype, characterized by bronchial epithelial dysfunction and upregulated antimicrobial and inflammatory response, resembles the immunophenotype of psoriasis, including activation of the thromboxane B2 pathway, which should be considered a biomarker for this phenotype in further studies, including clinical trials targeting IL-17.

Accumulating evidence highlights links between iron regulation and respiratory disease. Here, we assessed the relationship between iron levels and regulatory responses in clinical and experimental asthma.We show that cell-free iron levels are reduced in the bronchoalveolar lavage (BAL) supernatant of severe or mild–moderate asthma patients and correlate with lower forced expiratory volume in 1 s (FEV1). Conversely, iron-loaded cell numbers were increased in BAL in these patients and with lower FEV1/forced vital capacity (FVC) ratio. The airway tissue expression of the iron sequestration molecules divalent metal transporter 1 (DMT1) and transferrin receptor 1 (TFR1) are increased in asthma, with TFR1 expression correlating with reduced lung function and increased Type-2 (T2) inflammatory responses in the airways. Furthermore, pulmonary iron levels are increased in a house dust mite (HDM)-induced model of experimental asthma in association with augmented Tfr1 expression in airway tissue, similar to human disease. We show that macrophages are the predominant source of increased Tfr1 and Tfr1+ macrophages have increased Il13 expression. We also show that increased iron levels induce increased pro-inflammatory cytokine and/or extracellular matrix (ECM) responses in human airway smooth muscle (ASM) cells and fibroblasts ex vivo and induce key features of asthma in vivo, including airway hyper-responsiveness (AHR) and fibrosis, and T2 inflammatory responses.Together these complementary clinical and experimental data highlight the importance of altered pulmonary iron levels and regulation in asthma, and the need for a greater focus on the role and potential therapeutic targeting of iron in the pathogenesis and severity of disease.

Type-2 (T2) immune responses in airway epithelial cells (AECs) classifies mild–moderate asthma into a T2-high phenotype. We examined whether currently available clinical biomarkers can predict AEC-defined T2-high phenotype within the U-BIOPRED cohort.The transcriptomic profile of AECs obtained from brushings of 103 patients with asthma and 44 healthy controls was obtained and gene set variation analysis used to determine the relative expression score of T2 asthma using a signature from interleukin (IL)-13-exposed AECs.37% of asthmatics (45% nonsmoking severe asthma, n=49; 33% of smoking or ex-smoking severe asthma, n=18; and 28% mild–moderate asthma, n=36) were T2-high using AEC gene expression. They were more symptomatic with higher exhaled nitric oxide fraction (FeNO) and blood and sputum eosinophils, but not serum IgE or periostin. Sputum eosinophilia correlated best with the T2-high signature. FeNO (≥30 ppb) and blood eosinophils (≥300 cells·µL−1) gave a moderate prediction of T2-high asthma. Sputum IL-4, IL-5 and IL-13 protein levels did not correlate with gene expression.T2-high severe asthma can be predicted to some extent from raised levels of FeNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors. Better bedside biomarkers are needed to detect T2-high.

Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the image, tracing out a boundary of the ROI – using the magnitude difference of the Poincare map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset.

Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment. Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking. Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0-6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm. Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10-22). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10-8). Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.

Translational biomedical research has become a science driven by big data. Improving patient care by developing personalized therapies and new drugs depends increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of large-scale internal and external, partner and public, data sources. As analysing these large-scale and complex datasets has become increasingly computationally expensive, it is of paramount importance to enable researchers to seamlessly scale up their computation platform while being able to manage complex yet flexible scenario that biomedical scientists are asking for. We developed a new platform as an answer to those needs of analysing and exploring massive amounts of medical data with the constrain of enabling the broadest audience, ranging from the medical doctor to the advanced coders, to easily and intuitively exploit this new resource. The platform consists of three main components: Borderline UI, the eTRIKS Analytical Environment (eAE) and the eTRIKS Data Platform (eDP). Each component has been developed independently to address specific sets of problems, then, loosely connected to each other components as to form a coherent platform for large scale medical data analysis. The code is fully open source and available on GitHub at this address: https://github.com/dsi-icl

Severe asthma patients with a significant smoking history have airflow obstruction with reported neutrophilia. We hypothesise that multi-omic analysis will enable the definition of smoking and ex-smoking severe asthma molecular phenotypes.The U-BIOPRED cohort of severe asthma patients, containing current-smokers (CSA), ex-smokers (ESA), nonsmokers and healthy nonsmokers was examined. Blood and sputum cell counts, fractional exhaled nitric oxide and spirometry were obtained. Exploratory proteomic analysis of sputum supernatants and transcriptomic analysis of bronchial brushings, biopsies and sputum cells was performed.Colony-stimulating factor (CSF)2 protein levels were increased in CSA sputum supernatants, with azurocidin 1, neutrophil elastase and CXCL8 upregulated in ESA. Phagocytosis and innate immune pathways were associated with neutrophilic inflammation in ESA. Gene set variation analysis of bronchial epithelial cell transcriptome from CSA showed enrichment of xenobiotic metabolism, oxidative stress and endoplasmic reticulum stress compared to other groups. CXCL5 and matrix metallopeptidase 12 genes were upregulated in ESA and the epithelial protective genes, mucin 2 and cystatin SN, were downregulated.Despite little difference in clinical characteristics, CSA were distinguishable from ESA subjects at the sputum proteomic level, with CSA patients having increased CSF2 expression and ESA patients showing sustained loss of epithelial barrier processes.

The advertising industry depends on an effective assessment of the impact of advertising as a key performance metric for their products. However, current assessment methods have relied on either indirect inference from observing changes in consumer behavior after the launch of an advertising campaign, which has long cycle times and requires an ad campaign to have already have been launched (often meaning costs having been sunk). Or through surveys or focus groups, which have a potential for experimental biases, peer pressure, and other psychological and sociological phenomena that can reduce the effectiveness of the study. In this paper, we investigate a new approach to assess the impact of advertisement by utilizing low-cost EEG headbands to record and assess the measurable impact of advertising on the brain. Our evaluation shows the desired performance of our method based on user experiment with 30 recruited subjects after watching 220 different advertisements. We believe the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias.

The U-BIOPRED study is a multicentre European study aimed at a better understanding of severe asthma. It included three steroid-treated adult asthma groups (severe nonsmokers (SAn group), severe current/ex-smokers (SAs/ex group) and those with mild–moderate disease (MMA group)) and healthy controls (HC group). The aim of this cross-sectional, bronchoscopy substudy was to compare bronchial immunopathology between these groups.In 158 participants, bronchial biopsies and bronchial epithelial brushings were collected for immunopathologic and transcriptomic analysis. Immunohistochemical analysis of glycol methacrylate resin-embedded biopsies showed there were more mast cells in submucosa of the HC group (33.6 mm−2) compared with both severe asthma groups (SAn: 17.4 mm−2, p−2, p=0.01) and with the MMA group (21.2 mm−2, p=0.01). The number of CD4+lymphocytes was decreased in the SAs/ex group (4.7 mm−2) compared with the SAn (11.6 mm−2, p=0.002), MMA (10.1 mm−2, p=0.008) and HC (10.6 mm−2, pAffymetrix microarray analysis identified seven probe sets in the bronchial brushing samples that had a positive relationship with submucosal eosinophils. These mapped toCOX-2(cyclo-oxygenase-2),ADAM-7(disintegrin and metalloproteinase domain-containing protein 7),SLCO1A2(solute carrier organic anion transporter family member 1A2),TMEFF2(transmembrane protein with epidermal growth factor like and two follistatin like domains 2) andTRPM-1(transient receptor potential cation channel subfamily M member 1); the remaining two are unnamed.We conclude that in nonsmoking and smoking patients on currently recommended therapy, severe asthma exists despite suppressed tissue inflammation within the proximal airway wall.

To obtain information from quantitative data, we need to develop various analysis methods, which can be drawn from diverse fields, such as computer science, information theory and statistics. This chapter discusses methods for analysing datasets generated in asthma study for personalized medicine. Personalized medicine is the future of medicine, aiming at providing tailor-made medical decisions, practices and products to individual patients. Medical decisions and treatments are being tailored to individual patient based on the context of patient’s various profiles such as Genomics, Proteomics, Lipidomics and Metabolomics content. High throughput instruments are used to generate large scale datasets. To succeed in personalized medicine, analysis methods, including those dedicated to specific data types and those shared among various data, should be well developed. In this chapter, we first discuss the need of using data from molecular level to pathway level. Then we introduce analysis methods in typical analysis steps, which are batch effect detection and removal, statistical analysis, feature selection and classification, and unsupervised way of pattern recognition.

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

Translational research is quickly becoming a science driven by big data. Improving patient care, developing personalized therapies and new drugs depend increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of large-scale partner and public sources. As analysing these large-scale datasets becomes computationally increasingly expensive, traditional analytical engines are struggling to provide a timely answer to the questions that biomedical scientists are asking. Designing such a framework is developing for a moving target as the very nature of biomedical research based on big data requires an environment capable of adapting quickly and efficiently in response to evolving questions. The resulting framework consequently must be scalable in face of large amounts of data, flexible, efficient and resilient to failure. In this paper we design the eTRIKS Analytical Environment (eAE), a scalable and modular framework for the efficient management and analysis of large scale medical data, in particular the massive amounts of data produced by high-throughput technologies. We particularly discuss how we design the eAE as a modular and efficient framework enabling us to add new components or replace old ones easily. We further elaborate on its use for a set of challenging big data use cases in medicine and drug discovery.

Classification models built on biological expression data are increasingly used to predict distinct disease subtypes. Selected features that separate sample groups can be the candidates of biomarkers, helping us to discover biological functions/pathways. However, three challenges are associated with building a robust classification and feature selection model: 1) the number of significant biomarkers is much smaller than that of measured features for which the search will be exhaustive; 2) current biological expression data are big in both sample size and feature size which will worsen the scalability of any search algorithms; and 3) expression profiles of certain features are typically highly correlated which may prevent to distinguish the predominant features. Unfortunately, most of the existing algorithms are partially addressing part of these challenges but not as a whole. In this paper, we propose a unified framework to address the above challenges. The classification and feature selection problem is first formulated as a nonconvex optimisation problem. Then the problem is relaxed and solved iteratively by a sequence of convex optimisation procedures which can be distributed computed and therefore allows the efficient implementation on advanced infrastructures. To illustrate the competence of our method over others, we first analyse a randomly generated simulation dataset under various conditions. We then analyse a real gene expression dataset on embryonal tumour. Further downstream analysis, such as functional annotation and pathway analysis, are performed on the selected features which elucidate several biological findings.