Radiomultiomics: quantitative CT clusters of severe asthma associated with multi-omics.

RATIONALE: Lung quantitative computed tomographic (qCT) severe asthma clusters have been reported, but their replication and underlying disease mechanisms are unknown. We identified and replicated qCT clusters of severe asthma in two independent asthma cohorts and determined their association with molecular pathways. METHODS: We used consensus clustering on qCT measurements of airway and lung CT scans, performed in 105 severe asthmatic adults from the U-BIOPRED cohort. The same qCT measurements were used to replicate qCT clusters in a subsample of the ATLANTIS asthma cohort (n=97). We performed integrated enrichment analysis using blood, sputum, bronchial biopsies, bronchial brushings and nasal brushings transcriptomics and blood and sputum proteomics to characterize radiomultiomic-associated clusters (RACs). RESULTS: qCT clusters and clinical features in U-BIOPRED were replicated in the matched ATLANTIS cohort. In the U-BIOPRED cohort, RAC1 (n=30) was predominantly female with elevated BMI, mild airflow limitation, normal qCT parameters and upregulation of the complement pathway. RAC2 (n=34) subjects had a lower degree of airflow limitation, airway wall thickness and dilatation, with upregulation of proliferative pathways, including neurotrophic receptor tyrosine kinase 2/tyrosine kinase receptor B (NTRK2/TRKB), and down-regulation of semaphorin pathways. RAC3 (n=41) showed increased lung attenuation area and air trapping, severe airflow limitation, hyperinflation, and upregulation of cytokine signaling and signaling by interleukin pathways, and matrix metallopeptidase 1, 2 and 9. CONCLUSIONS: U-BIOPRED severe asthma qCT clusters were replicated in a matched independent asthmatic cohort and associated with specific molecular pathways. Radiomultiomics might represent anovel strategy to identify new molecular pathways in asthma pathobiology.

Th2 high and mast cell gene signatures are associated with corticosteroid sensitivity in COPD.

RATIONALE: Severe asthma and chronic obstructive pulmonary disease (COPD) share common pathophysiological 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 Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort comprising one Th2-high inflammatory signature (TAC1) and two Th2-low signatures (TAC2 and TAC3). OBJECTIVE: We examined whether gene expression signatures obtained in asthma can be used to identify the subgroup of patients with COPD with steroid sensitivity. METHODS: Using gene set variation analysis, 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 Groningen Leiden Universities Corticosteroids in Obstructive Lung Disease COPD study that received 30 months of treatment with inhaled corticosteroids (ICS) with and without an added long-acting ß-agonist (LABA). The identified signatures were then associated with longitudinal clinical variables after treatment. Differential gene expression and cellular convolution were used to define key regulated genes and cell types. MEASUREMENTS AND MAIN RESULTS: Bronchial biopsies in patients with COPD at baseline showed a wide range of expression of the 3 TAC signatures. 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 signature was developed from the TAC1 ICS-responsive genes. This signature consisted of mast cell-specific genes identified by single-cell RNA-sequencing and positively correlated with bronchial biopsy mast cell numbers following ICS±LABA. Baseline levels of gene transcription correlated with the change in RV/TLC %predicted following 30-month ICS±LABA. CONCLUSION: Sputum-derived transcriptomic signatures from an asthma cohort can be recapitulated in bronchial biopsies of patients with COPD and identified a signature of airway mast cells as a predictor of corticosteroid responsiveness.

Macrophage migration inhibitory factor promotes glucocorticoid resistance of neutrophilic inflammation in a murine model of severe asthma.

BACKGROUND: Severe neutrophilic asthma is resistant to treatment with glucocorticoids. The immunomodulatory protein macrophage migration inhibitory factor (MIF) promotes neutrophil recruitment to the lung and antagonises responses to glucocorticoids. We hypothesised that MIF promotes glucocorticoid resistance of neutrophilic inflammation in severe asthma. METHODS: We examined whether sputum MIF protein correlated with clinical and molecular characteristics of severe neutrophilic asthma in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort. We also investigated whether MIF regulates neutrophilic inflammation and glucocorticoid responsiveness in a murine model of severe asthma in vivo. RESULTS: MIF protein levels positively correlated with the number of exacerbations in the previous year, sputum neutrophils and oral corticosteroid use across all U-BIOPRED subjects. Further analysis of MIF protein expression according to U-BIOPRED-defined transcriptomic-associated clusters (TACs) revealed increased MIF protein and a corresponding decrease in annexin-A1 protein in TAC2, which is most closely associated with airway neutrophilia and NLRP3 inflammasome activation. In a murine model of severe asthma, treatment with the MIF antagonist ISO-1 significantly inhibited neutrophilic inflammation and increased glucocorticoid responsiveness. Coimmunoprecipitation studies using lung tissue lysates demonstrated that MIF directly interacts with and cleaves annexin-A1, potentially reducing its biological activity. CONCLUSION: Our data suggest that MIF promotes glucocorticoid-resistance of neutrophilic inflammation by reducing the biological activity of annexin-A1, a potent glucocorticoid-regulated protein that inhibits neutrophil accumulation at sites of inflammation. This represents a previously unrecognised role for MIF in the regulation of inflammation and points to MIF as a potential therapeutic target for the management of severe neutrophilic asthma.

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.

The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This 'black box' problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE)-based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but also the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning-based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.

Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19.

Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number Rt during emerging epidemics, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.

Analyzing drop coalescence in microfluidic devices with a deep learning generative model.

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering.

Association of Differential Mast Cell Activation with Granulocytic Inflammation in Severe Asthma.

Rationale: Mast cells (MCs) play a role in inflammation and both innate and adaptive immunity, but their involvement in severe asthma (SA) remains undefined. Objectives: We investigated the phenotypic characteristics of the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes) asthma cohort by applying published MC activation signatures to the sputum cell transcriptome. Methods: Eighty-four participants with SA, 20 with mild/moderate asthma (MMA), and 16 healthy participants without asthma were studied. We calculated enrichment scores (ESs) for nine MC activation signatures by asthma severity, sputum granulocyte status, and three previously defined sputum molecular phenotypes or transcriptome-associated clusters (TACs) 1, 2, and 3 using gene set variation analysis. Measurements and Main Results: MC signatures except unstimulated, repeated FcεR1-stimulated and IFN-γ-stimulated signatures were enriched in SA. A FcεR1-IgE-stimulated and a single-cell signature from asthmatic bronchial biopsies were highly enriched in eosinophilic asthma and in the TAC1 molecular phenotype. Subjects with a high ES for these signatures had elevated sputum amounts of similar genes and pathways. IL-33- and LPS-stimulated MC signatures had greater ES in neutrophilic and mixed granulocytic asthma and in the TAC2 molecular phenotype. These subjects exhibited neutrophil, NF-κB (nuclear factor-κB), and IL-1ß/TNF-α (tumor necrosis factor-α) pathway activation. The IFN-γ-stimulated signature had the greatest ES in TAC2 and TAC3 that was associated with responses to viral infection. Similar results were obtained in an independent ADEPT (Airway Disease Endotyping for Personalized Therapeutics) asthma cohort. Conclusions: Gene signatures of MC activation allow the detection of SA phenotypes and indicate that MCs can be induced to take on distinct transcriptional phenotypes associated with specific clinical phenotypes. IL-33-stimulated MC signature was associated with severe neutrophilic asthma, whereas IgE-activated MC was associated with an eosinophilic phenotype.

Relationship between type 2 cytokine and inflammasome responses in obesity-associated asthma.

BACKGROUND: Obesity is a risk factor for asthma, and obese asthmatic individuals are more likely to have severe, steroid-insensitive disease. How obesity affects the pathogenesis and severity of asthma is poorly understood. Roles for increased inflammasome-mediated neutrophilic responses, type 2 immunity, and eosinophilic inflammation have been described. OBJECTIVE: We investigated how obesity affects the pathogenesis and severity of asthma and identified effective therapies for obesity-associated disease. METHODS: We assessed associations between body mass index and inflammasome responses with type 2 (T2) immune responses in the sputum of 25 subjects with asthma. Functional roles for NLR family, pyrin domain-containing (NLRP) 3 inflammasome and T2 cytokine responses in driving key features of disease were examined in experimental high-fat diet-induced obesity and asthma. RESULTS: Body mass index and inflammasome responses positively correlated with increased IL-5 and IL-13 expression as well as C-C chemokine receptor type 3 expression in the sputum of subjects with asthma. High-fat diet-induced obesity resulted in steroid-insensitive airway hyperresponsiveness in both the presence and absence of experimental asthma. High-fat diet-induced obesity was also associated with increased NLRP3 inflammasome responses and eosinophilic inflammation in airway tissue, but not lumen, in experimental asthma. Inhibition of NLRP3 inflammasome responses reduced steroid-insensitive airway hyperresponsiveness but had no effect on IL-5 or IL-13 responses in experimental asthma. Depletion of IL-5 and IL-13 reduced obesity-induced NLRP3 inflammasome responses and steroid-insensitive airway hyperresponsiveness in experimental asthma. CONCLUSION: We found a relationship between T2 cytokine and NLRP3 inflammasome responses in obesity-associated asthma, highlighting the potential utility of T2 cytokine-targeted biologics and inflammasome inhibitors.

Mapping atopic dermatitis and anti-IL-22 response signatures to type 2-low severe neutrophilic asthma.

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, TH2, and TH17/TH22 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 < .05). The FZ-response signature (296 downregulated genes) was enriched in asthmatic blood (P < .05) and particularly in neutrophilic and mixed granulocytic sputum (P < .05). These data were confirmed in sputum of the Airway Disease Endotyping for Personalized Therapeutics cohort. IL-22 mRNA across tissues did not correlate with FZ-response enrichment scores, but this response signature correlated with TH22/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.

Afatinib treatment in a large real-world cohort of nonsmall cell lung cancer patients with common and uncommon epidermal growth factor receptor mutation.

The epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) afatinib improves survival in nonsmall cell lung cancer (NSCLC) patients with EGFR mutation. We analysed the outcome between EGFR mutation subtypes in a large afatinib-treated cohort in which 516 EGFR-mutated NSCLC patients receiving afatinib as front-line treatment. EGFR uncommon mutations include exon 20 insertion, de novo T790M of high or low allele frequency (dT790MHAF /dT790MLAF ), non-T790M compound mutation and others, where EGFR exon 20 insertion and dT790MHAF were defined as type-I and the rest as type-II uncommon mutation. Four hundred and sixty-one (89.3%) and 55 (10.7%) patients were common and uncommon mutation, respectively. Exon 20 insertion and dT790MHAF patients demonstrated a significantly shortened progression-free survival (PFS) (2.6 and 4.1 months) compared to EGFR common mutation, dT790MLAF and other uncommon mutation patients (15.1, 27.0 and 18.4 months; P = 3 × 10-8 ). Type-I uncommon mutation was an independent predictor of PFS (HR 4.46 [95% CI, 2.60-7.64]; P < .001) and OS (HR 2.56 [95% CI, 1.37-4.75]; P = .003). EGFR L858R patients demonstrated a significantly higher CNS progression (cause-specific HR, 3.16; 95% CI 1.24-8.08; P = .016), and type-I uncommon mutation patients exhibited a significantly higher systemic progression (cause-specific HR, 4.95; 95% CI 2.30-10.60; P = 4.3 × 10-5 ). Tendencies of higher CNS and lower systemic progression were observed in type-II uncommon mutation patients. A PFS ≥ 12 months (OR 2.38 [95% CI, 1.18-4.89]; P = .016) and uncommon EGFR mutation (OR 0.08 [95% CI, 0.01-0.48]; P = .021) were independent predictors of secondary T790M. Afatinib-treated NSCLC patients presented an EGFR genotype-specific pattern of disease progression and outcome.

Network-based visualisation reveals new insights into transposable element diversity.

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.

Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic.

The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus, there is an urgent need to study the dynamics of epidemiological models to gain a better understanding of how such diseases spread. While epidemiological models can be computationally expensive, recent advances in machine learning techniques have given rise to neural networks with the ability to learn and predict complex dynamics at reduced computational costs. Here we introduce two digital twins of a SEIRS model applied to an idealised town. The SEIRS model has been modified to take account of spatial variation and, where possible, the model parameters are based on official virus spreading data from the UK. We compare predictions from one digital twin based on a data-corrected Bidirectional Long Short-Term Memory network with predictions from another digital twin based on a predictive Generative Adversarial Network. The predictions given by these two frameworks are accurate when compared to the original SEIRS model data. Additionally, these frameworks are data-agnostic and could be applied to towns, idealised or real, in the UK or in other countries. Also, more compartments could be included in the SEIRS model, in order to study more realistic epidemiological behaviour.

A Metabolite Array Technology for Precision Medicine.

The application of metabolomics in translational research suffers from several technological bottlenecks, such as data reproducibility issues and the lack of standardization of sample profiling procedures. Here, we report an automated high-throughput metabolite array technology that can rapidly and quantitatively determine 324 metabolites including fatty acids, amino acids, organic acids, carbohydrates, and bile acids. Metabolite identification and quantification is achieved using the Targeted Metabolome Batch Quantification (TMBQ) software, the first cross-vendor data processing pipeline. A test of this metabolite array was performed by analyzing serum samples from patients with chronic liver disease (N = 1234). With high detection efficiency and sensitivity in serum, urine, feces, cell lysates, and liver tissue samples and suitable for different mass spectrometry systems, this metabolite array technology holds great potential for biomarker discovery and high throughput clinical testing. Additionally, data generated from such standardized procedures can be used to generate a clinical metabolomics database suitable for precision medicine in next-generation healthcare.

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.

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.

Sputum ACE2, TMPRSS2 and FURIN gene expression in severe neutrophilic asthma.

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.

Sputum macrophage diversity and activation in asthma: Role of severity and inflammatory phenotype.

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 < .01). Sputum macrophage number and the ES for most macrophage signatures were higher in the TAC3 group compared to TAC1 and TAC2 asthmatics. However, a high enrichment was found in TAC1 for 3 modules showing inflammatory pathways linked to Toll-like and TNF receptor activation and arachidonic acid metabolism (P < .001) and in TAC2 for the inflammasome and interferon signalling pathways (P < .001). Data were validated in the ADEPT cohort. Module analysis provides additional information compared to conventional M1 and M2 classification. TR-Mφ were enriched in TAC3 and associated with mitochondrial function. CONCLUSIONS: Macrophage activation is attenuated in severe granulocytic asthma highlighting defective innate immunity except for specific subsets characterized by distinct inflammatory pathways.

A retrospective study of alectinib versus ceritinib in patients with advanced non-small-cell lung cancer of anaplastic lymphoma kinase fusion in whom crizotinib treatment failed.

BACKGROUND: Crizotinib is the approved treatment for advanced non-small cell lung cancers (NSCLCs) of anaplastic lymphoma kinase (ALK) fusion. Failure of crizotinib treatment frequently involves drug intolerance or resistance. Comparison of using second-generation ALK inhibitors in this setting remains lacking. METHODS: Sixty-five ALK-positive advanced NSCLC patients receiving second-generation ALK inhibitors following treatment failure of crizotinib were retrospectively analyzed for the therapeutic efficacy. RESULTS: Forty-three (66.2%) and 22 (33.8%) patients received alectinib and ceritinib, respectively. Comparing alectinib to ceritinib treatment: the 12-month progression-free survival (PFS) rate (61.0% [95% confidence interval, 47.1 to 78.9%] vs. 54.5% [95% CI, 37.3 to 79.9%]); the hazard ratio (HR) for disease progression or death, 0.61 (95% CI, 0.31-1.17; p = 0.135). Multivariate Cox regression showed ECOG PS (0-1 vs. 2-3 HR 0.09 [95% CI, 0.02-0.33]; p < 0.001) and cause of crizotinib treatment failure (resistance vs. intolerance HR 2.75 [95% CI, 1.26-5.99]; p = 0.011) were the independent predictors for the PFS of second-generation ALK inhibitors. Treatment of alectinib, compared to ceritinib, was associated with a lower incidence of CNS progression (cause-specific HR, 0.10; 95% CI 0.01-0.78; p = 0.029) and a higher efficacy in patients whose cause of crizotinib treatment failure was intolerance (HR 0.29 [95% CI, 0.08-1.06]; p = 0.050). The most commonly noted adverse events were elevated AST/ALT in 10 (23.3%) patients treated with alectinib and diarrhea in 8 (36.4%) patients treated with ceritinib. CONCLUSION: Second-generation ALK inhibitors in crizotinib-treated patients showed a satifactory efficacy. Alectinib treatment demonstrated a CNS protection activity and a higher PFS in selected patients failing crizotinib treatment.

Vitamin D Metabolism Is Dysregulated in Asthma and Chronic Obstructive Pulmonary Disease.

Rationale: Vitamin D deficiency is common in patients with asthma and chronic obstructive pulmonary disease (COPD). Low 25-hydroxyvitamin D (25[OH]D) levels may represent a cause or a consequence of these conditions.Objectives: 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 1 year, differed between those with asthma or COPD versus control subjects. Serum concentrations of vitamin D3, 25(OH)D3, and 1α,25-dihydroxyvitamin D3 (1α,25[OH]2D3) were determined presupplementation and postsupplementation 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 14 datasets to compare expression of 1α,25(OH)2D3-inducible gene expression signatures in clinical samples taken from adults with asthma or COPD versus control subjects.Measurements and Main Results: The mean postsupplementation 25(OH)D increase in participants with asthma (20.9 nmol/L) and COPD (21.5 nmol/L) was lower than in control subjects (39.8 nmol/L; P = 0.001). Compared with control subjects, 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 presupplementation and postsupplementation (P ≤ 0.005). Intergroup differences in 1α,25(OH)2D3-inducible gene expression signatures were modest and variable if 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.

Modeling of suppression and mitigation interventions in the COVID-19 epidemics.

BACKGROUND: The global spread of the COVID-19 pandemic has become the most fundamental threat to human health. In the absence of vaccines and effective therapeutical solutions, non-pharmaceutic intervention has become a major way for controlling the epidemic. Gentle mitigation interventions are able to slow down the epidemic but not to halt it well. While strict suppression interventions are efficient for controlling the epidemic, long-term measures are likely to have negative impacts on economics and people's daily live. Hence, dynamically balancing suppression and mitigation interventions plays a fundamental role in manipulating the epidemic curve. METHODS: We collected data of the number of infections for several countries during the COVID-19 pandemics and found a clear phenomenon of periodic waves of infection. Based on the observation, by connecting the infection level with the medical resources and a tolerance parameter, we propose a mathematical model to understand impacts of combining intervention measures on the epidemic dynamics. RESULTS: Depending on the parameters of the medical resources, tolerance level, and the starting time of interventions, the combined intervention measure dynamically changes with the infection level, resulting in a periodic wave of infections controlled below an accepted level. The study reveals that, (a) with an immediate, strict suppression, the numbers of infections and deaths are well controlled with a significant reduction in a very short time period; (b) an appropriate, dynamical combination of suppression and mitigation may find a feasible way in reducing the impacts of epidemic on people's live and economics. CONCLUSIONS: While the assumption of interventions deployed with a cycle of period in the model is limited and unrealistic, the phenomenon of periodic waves of infections in reality is captured by our model. These results provide helpful insights for policy-makers to dynamically deploy an appropriate intervention strategy to effectively battle against the COVID-19.

Crucial role for lung iron level and regulation in the pathogenesis and severity of asthma.

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.