The Role of Procalcitonin in Predicting Failure of Non-operative Management in Bowel Obstructions.

Procalcitonin has been investigated as a marker for bowel ischemia. This study examined the role of procalcitonin in predicting failure of non-operative management (NOM) in bowel obstructions. Patients with bowel obstructions at a single center from August 2022 to January 2023 were prospectively enrolled (n = 79). Lactic acid (LA) and procalcitonin were collected after surgical consultation. The primary outcome was success or failure of NOM. Univariate analysis, multivariable logistic regression, and performance measures of procalcitonin and LA in predicting bowel ischemia was performed. Of 79 patients included, 48 (61%) required operative intervention during index admission. There were no significant differences in demographics, comorbidities, procalcitonin, nor LA between groups. Time from last bowel movement was associated with failure of NOM (OR 1.03 [95% CI 1.01-1.06]; P = .008), though initial procalcitonin or LA was not. Procalcitonin >.3 ng/mL had acceptable sensitivity in screening for bowel ischemia.

Using an electronic diary and wristband accelerometer to detect exacerbations and activity levels in COPD: a feasibility study.

Background: Early and accurate identification of acute exacerbations of COPD may lead to earlier treatment and prevent hospital admission. Electronic diaries have been developed for symptom monitoring and accelerometers to monitor activity. However, it is unclear whether this technology is usable in the COPD population. This study aimed to assess the feasibility of an electronic diary (eDiary) for symptom reporting using the MoreCare app and activity monitoring with the Garmin Vivofit 2 in COPD. Methods: Participants were recruited from the London COPD Cohort. Participants were provided a Garmin Vivofit 2 activity monitor and an android tablet with the MoreCare app for a period of 3 months. Results: 25 COPD patients were recruited (mean±sd age 70.8±7.1 years, forced expiratory volume in 1 s (FEV1) 49.8±14.8% predicted). Age, gender, disease severity and exacerbation frequency had no impact on eDiary compliance. There was a moderate positive correlation between median daily very active minutes and FEV1 % pred (ρ=0.62, p=0.005). Daily step counts decreased during the initial 7 days of exacerbation and recovery compared to a pre-exacerbation baseline. A decision-tree model identified change in sputum colour, change in step count, severity of cold, exacerbation history and use of rescue medication as the most important predictors of acute exacerbations of COPD in this cohort. Conclusions: Symptom and activity monitoring using digital technology is feasible in COPD. Further large-scale digital health studies are needed to assess whether eDiaries can be used to identify patients at risk of exacerbation and guide early intervention.

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.

Recommendations to improve use and reporting of statistics in animal experiments.

Flaws in experimental statistics are a major contributor to the poor reproducibility of animal experiments. Informed decisions about whether conclusions are justified requires clear reporting of experimental data and the statistical methods used to analyse them. When data are misinterpreted, manipulated or concealed to generate publications, it creates an illusion that chance observations are robust data which confirm the hypotheses presented. Attempts to reproduce and advance such observations can propagate large areas of irreproducible science. This hinders scientific progress, erodes public support for research, damages reputations and wastes resources. This review analyses and explains recommendations to improve use and reporting of statistics in animal experiments.

A Retrospective Cohort Analysis of the Use of Enteral Nutrition Plus Pharmacologic Prophylaxis or Enteral Nutrition Alone.

Background: Due to the risk of development of stress ulcers in intensive care unit (ICU) patients, pharmacologic prophylaxis is often utilized. However, some literature describes the use of enteral nutrition instead as stress ulcer prophylaxis. Methods: The purpose of this study is to determine if enteral nutrition is similar to pharmacologic stress ulcer prophylaxis (SUP) with enteral nutrition for reduction of gastrointestinal (GI) bleeding, perforation, or ulceration in ICU patients. This was a retrospective, single-center cohort study that took place at an academic medical center. Adult ICU patients receiving enteral nutrition who had a risk factor for stress-related mucosal damage were included. The primary outcome was the incidence of GI bleeding, perforation, or ulcer formation. Results: Overall, 167 patients were included in the study, 147 in the pharmacologic prophylaxis plus EN group (PPEN) and 20 in the enteral therapy only (EN) group. Of 167 patients included, 22 patients (21 in the PPEN group and 1 in the EN group) developed a primary outcome of GI bleeding, perforation, or ulceration (14.3% vs 5%, P = .4781). Patients in the PPEN group had a higher incidence of pneumonia (42.2% vs 15%, P = .0194), but no difference was seen between groups when patients with pneumonia present on admission were excluded (20.6% vs 10.5%, P = .5254). Conclusion: In this small cohort of patients, enteral nutrition alone is as effective as pharmacologic therapy in addition to enteral nutrition for the reduction of stress-related GI bleeding, perforation, and ulceration.

I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment.

Crohn's disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions. We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment. We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohn's disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohn's disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enriched, respectively, suggesting greater molecular dysregulation in TNF non-responders at baseline. This methodological approach could facilitate better targeted design of clinical studies to test therapeutics, concentrating on patient subsets sharing similar underlying pathobiology, therefore increasing the likelihood of clinical response.

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium.

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.

Stratification of asthma phenotypes by airway proteomic signatures.

BACKGROUND: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. OBJECTIVE: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. METHODS: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. RESULTS: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms. CONCLUSION: This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies.

Contribution of airway eosinophils in airway wall remodeling in asthma: Role of MMP-10 and MET.

BACKGROUND: Eosinophils play an important role in the pathophysiology of asthma being implicated in airway epithelial damage and airway wall remodeling. We determined the genes associated with airway remodeling and eosinophilic inflammation in patients with asthma. METHODS: We analyzed the transcriptomic data from bronchial biopsies of 81 patients with moderate-to-severe asthma of the U-BIOPRED cohort. Expression profiling was performed using Affymetrix arrays on total RNA. Transcription binding site analysis used the PRIMA algorithm. Localization of proteins was by immunohistochemistry. RESULTS: Using stringent false discovery rate analysis, MMP-10 and MET were significantly overexpressed in biopsies with high mucosal eosinophils (HE) compared to low mucosal eosinophil (LE) numbers. Immunohistochemical analysis confirmed increased expression of MMP-10 and MET in bronchial epithelial cells and in subepithelial inflammatory and resident cells in asthmatic biopsies. Using less-stringent conditions (raw P-value < 0.05, log2 fold change > 0.5), we defined a 73-gene set characteristic of the HE compared to the LE group. Thirty-three of 73 genes drove the pathway annotation that included extracellular matrix (ECM) organization, mast cell activation, CC-chemokine receptor binding, circulating immunoglobulin complex, serine protease inhibitors, and microtubule bundle formation pathways. Genes including MET and MMP10 involved in ECM organization correlated positively with submucosal thickness. Transcription factor binding site analysis identified two transcription factors, ETS-1 and SOX family proteins, that showed positive correlation with MMP10 and MET expression. CONCLUSION: Pathways of airway remodeling and cellular inflammation are associated with submucosal eosinophilia. MET and MMP-10 likely play an important role in these processes.

Contrast-Induced Nephropathy in Ischemic Stroke Patients Undergoing Computed Tomography Angiography: CINISter Study.

GOAL: Computed tomography angiography (CTA) is a well-tolerated, noninvasive study of the intracranial vascular circulation; however, contrast-induced nephropathy (CIN) has been reported in 5%-7% of patients undergoing CTA. Limited studies have evaluated the risks of CIN in patients undergoing CTA. Our study was designed to evaluate the prevalence and risk factors for CIN in patients with ischemic stroke who receive a CTA. MATERIALS AND METHODS: Single-center, nested, case-control study of patients with ischemic stroke who received a CTA between June 18, 2012 and January 1, 2016. Patients were grouped based on development of CIN. FINDINGS: A total of 209 patients were included in the final analysis (178 controls, 31 cases). The prevalence of CIN during the time period studied was 14.8% (95% confidence interval [CI]: 10.2-20.2). A higher proportion of patients who developed CIN had a history of diabetes mellitus (37 [20.56%] versus 15 [48.39%]; P = .0009) and reported taking no medications prior to admission (35 [19.44%] versus 11 [35.48%]; P = .0458). However, a lower proportion of patients who developed CIN had a history of smoking (59 [32.78] versus 3 [9.68]; P = .0091). After statistical adjustment, only a history of diabetes (odds ratio [OR] 4.15 [95% CI: 1.765, 9.754), taking no medications prior to admission (OR 3.56 [95% CI: 1.417, 8.941]) and a self-reported history of smoking (OR 0.204 [95% CI: 0.057, 0.721]) remained associated with the development of CIN. CONCLUSIONS: Those patients with a history of diabetes mellitus or not taking medications prior to admission should be monitored closely for the development of contrast-induced nephropathy CIN.

U-BIOPRED: evaluation of the value of a public-private partnership to industry.

Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) was initiated in the first year of the Innovative Medicines Initiative (IMI). It was an ambitious plan to tackle the understanding of asthma through an integration of clinical and multi-'omics approaches that necessitated the bringing together of industry, academic, and patient representatives because it was too large to be managed by any one of the partners in isolation. It was a novel experience for all concerned. In this review, we describe the main features of the U-BIOPRED experience from the industry perspective. We list some of the key advantages and learnings from the perspective of the authors, and also improvements that we feel could be made in future projects.

Large-Scale Label-Free Quantitative Mapping of the Sputum Proteome.

Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMSE applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The "core" sputum proteome (proteins detected in ≥40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMSE is influenced by several factors, with some proteins being measured in all participants' samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Corrigendum: The RA-MAP Consortium: a working model for academia-industry collaboration.

This corrects the article DOI: 10.1038/nrrheum.2017.200.

The RA-MAP Consortium: a working model for academia-industry collaboration.

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.

Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma.

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 < 0.05). The inflammasome proteins nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing 1 (NLRP1), NLRP3, and nucleotide-binding oligomerization domain (NOD)-like receptor C4 (NLRC4) were associated with neutrophilic asthma and with sputum IL-1ß protein levels, whereas eosinophilic asthma was associated with an IL-13-induced TH2 signature and IL-1 receptor-like 1 (IL1RL1) mRNA expression. These differences were sputum specific because no activation of NLRP3 or enrichment of IL-1R family genes in bronchial brushings or biopsy specimens in patients with SA was observed. Expression of NLRP3 and of the IL-1R family genes was validated in the Airway Disease Endotyping for Personalized Therapeutics cohort. Inflammasome inhibition using CRID3 prevented airway hyperresponsiveness and airway inflammation (both neutrophilia and eosinophilia) in a mouse model of severe allergic asthma. CONCLUSION: IL1RL1 gene expression is associated with eosinophilic SA, whereas NLRP3 inflammasome expression is highest in patients with neutrophilic SA. TH2-driven eosinophilic inflammation and neutrophil-associated inflammasome activation might represent interacting pathways in patients with SA.

T-helper cell type 2 (Th2) and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in U-BIOPRED.

Asthma is characterised by heterogeneous clinical phenotypes. Our objective was to determine molecular phenotypes of asthma by analysing sputum cell transcriptomics from 104 moderate-to-severe asthmatic subjects and 16 nonasthmatic subjects.After filtering on the differentially expressed genes between eosinophil- and noneosinophil-associated sputum inflammation, we used unbiased hierarchical clustering on 508 differentially expressed genes and gene set variation analysis of specific gene sets.We defined three transcriptome-associated clusters (TACs): TAC1 (characterised by immune receptors IL33R, CCR3 and TSLPR), TAC2 (characterised by interferon-, tumour necrosis factor-α- and inflammasome-associated genes) and TAC3 (characterised by genes of metabolic pathways, ubiquitination and mitochondrial function). TAC1 showed the highest enrichment of gene signatures for interleukin-13/T-helper cell type 2 (Th2) and innate lymphoid cell type 2. TAC1 had the highest sputum eosinophilia and exhaled nitric oxide fraction, and was restricted to severe asthma with oral corticosteroid dependency, frequent exacerbations and severe airflow obstruction. TAC2 showed the highest sputum neutrophilia, serum C-reactive protein levels and prevalence of eczema. TAC3 had normal to moderately high sputum eosinophils and better preserved forced expiratory volume in 1 s. Gene-protein coexpression networks from TAC1 and TAC2 extended this molecular classification.We defined one Th2-high eosinophilic phenotype TAC1, and two non-Th2 phenotypes TAC2 and TAC3, characterised by inflammasome-associated and metabolic/mitochondrial pathways, respectively.

U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics.

BACKGROUND: Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalized management approach can be provided. OBJECTIVES: We stratified patients with moderate-to-severe asthma based on clinicophysiologic parameters and performed an omics analysis of sputum. METHODS: Partition-around-medoids clustering was applied to a training set of 266 asthmatic participants from the European Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) adult cohort using 8 prespecified clinic-physiologic variables. This was repeated in a separate validation set of 152 asthmatic patients. The clusters were compared based on sputum proteomics and transcriptomics data. RESULTS: Four reproducible and stable clusters of asthmatic patients were identified. The training set cluster T1 consists of patients with well-controlled moderate-to-severe asthma, whereas cluster T2 is a group of patients with late-onset severe asthma with a history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of nonsmokers. Cluster T4 is predominantly composed of obese female patients with uncontrolled severe asthma with increased exacerbations but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters (T2, T3, and T4) had higher sputum eosinophilia than cluster T1, with no differences in sputum neutrophil counts and exhaled nitric oxide and serum IgE levels. CONCLUSION: Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.