Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.

BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.This work was supported by grants from the German Federal Ministry of Education and Research (BMBF), by BMBF Grant No. 01ZX1313C (project e:Athero-MED) and Grant No. 03IS2061B (project Gani_Med). Moreover, the research leading to these results has received funding from the European Union’s Seventh Framework Programme [FP7-Health-F5-2012] under grant agreement No. 305280 (MIMOmics) and from the European Research Council (starting grant “LatentCauses”). KS is supported by Biomedical Research Program funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. The KORA Augsburg studies were financed by the Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany and supported by grants from the German Federal Ministry of Education and Research (BMBF). Analyses in the EPIC-Norfolk study were supported by funding from the Medical Research Council (MC_PC_13048 and MC_UU_12015/1)

LocTree3 prediction of localization

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Recent Advances in Molecular and Immunological Diagnostic Platform for Virus Detection: A Review

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing coronavirus disease (COVID-19) outbreak and a rising demand for the development of accurate, timely, and cost-effective diagnostic tests for SARS-CoV-2 as well as other viral infections in general. Currently, traditional virus screening methods such as plate culturing and real-time PCR are considered the gold standard with accurate and sensitive results. However, these methods still require sophisticated equipment, trained personnel, and a long analysis time. Alternatively, with the integration of microfluidic and biosensor technologies, microfluidic-based biosensors offer the ability to perform sample preparation and simultaneous detection of many analyses in one platform. High sensitivity, accuracy, portability, low cost, high throughput, and real-time detection can be achieved using a single platform. This review presents recent advances in microfluidic-based biosensors from many works to demonstrate the advantages of merging the two technologies for sensing viruses. Different platforms for virus detection are classified into two main sections: immunoassays and molecular assays. Moreover, available commercial sensing tests are analyzed

Recent Progress in Nanotechnology-Based Approaches for Food Monitoring

Throughout the food supply chain, including production, storage, and distribution, food can be contaminated by harmful chemicals and microorganisms, resulting in a severe threat to human health. In recent years, the rapid advancement and development of nanotechnology proposed revolutionary solutions to solve several problems in scientific and industrial areas, including food monitoring. Nanotechnology can be incorporated into chemical and biological sensors to improve analytical performance, such as response time, sensitivity, selectivity, reliability, and accuracy. Based on the characteristics of the contaminants and the detection methods, nanotechnology can be applied in different ways in order to improve conventional techniques. Nanomaterials such as nanoparticles, nanorods, nanosheets, nanocomposites, nanotubes, and nanowires provide various functions for the immobilization and labeling of contaminants in electrochemical and optical detection. This review summarizes the recent advances in nanotechnology for detecting chemical and biological contaminations in the food supply chain

Design Strategy and Application of Deep Eutectic Solvents for Green Synthesis of Nanomaterials

The first report of deep eutectic solvents (DESs) was released in 2003 and was identified as a new member of ionic liquid (IL), involving innovative chemical and physical characteristics. Using green solvent technology concerning economical, practical, and environmental aspects, DESs open the window for sustainable development of nanomaterial fabrication. The DESs assist in different fabrication processes and design nanostructures with specific morphology and properties by tunable reaction conditions. Using DESs in synthesis reactions can reduce the required high temperature and pressure conditions for decreasing energy consumption and the risk of environmental contamination. This review paper provides the recent applications and advances in the design strategy of DESs for the green synthesis of nanomaterials. The strategy and application of DESs in wet-chemical processes, nanosize reticular material fabrication, electrodeposition/electrochemical synthesis of nanostructures, electroless deposition, DESs based nano-catalytic and nanofluidic systems are discussed and highlighted in this review

Qatar Metabolomics Study on Diabetes

The Qatar Metabolomics Study on Diabetes (QMDiab) is a type 2 diabetes case-control, which was conducted in 2012 at the Dermatology Department of Hamad Medical Corporation and the Weill Cornell Medical College in Doha, Qatar. The study was approved by the Institutional Review Boards of HMC and WCM-Q under research protocol number 11131/11. All study participants provided written informed consent.<br><br>Untargeted metabolomics measurements (LC/MS+, LC/MS-, and GC/MS) from plasma, urine, and saliva samples of 374 participants, which are aged 17-81 years, were performed by Metabolon Inc.<br><br>The OrigScale dataset comprises median-scaled data for each body fluid. The Preprocessed dataset comprises missing values treated, normalized, transformed, and scaled data. In both datasets, rows correspond to participants (anonymized) and columns correspond to metabolites. <br><br>Phenotype information (type 2 diabetes status, age, gender, BMI, ethnicity) are available as the last six columns of each dataset.<br>Annotations and pathway assigments are also provided for each metabolite

Optimization of Parameters for the Extraction of Phenolic Antioxidants from Boxberry Tree (Myrica Esculenta) Bark Using Response Surface Methodology

The boxberry tree (Myrica esculenta) bark has been known to have multiple health benefits and is used as a traditional medicine. A critical gap in knowledge exists on a simple but effective method to isolate the bioactive components from the bark. This study aimed to optimize the operating conditions, including temperature, ethanol concentration, and time, for the extraction of phenolic antioxidants from the boxberry bark sample using a response surface methodology. Results showed that the second-order polynomial regression models were statistically significant and sufficient to estimate the responses. Response surface optimization for all responses was successfully carried out to determine the optimum extraction conditions, which were a temperature, an ethanol concentration, and an extraction time of 75.8 °C, 48.3% (v/v), and 117 min, respectively. At these conditions, total phenolic and total flavonoid contents, 3-ethylbenzothiazoline-6-sulphonic acid diammonium salt (ABTS) scavenging capacity, and ferric-reducing antioxidant power were predicted to be 205.9 mg GAE/100 g, 37.8 mg CE/100 g, 271.3 mg AAE/100 g, and 111.4 mg AAE/100 g, respectively. The insignificant difference between the estimated and the experimental values suggested that the predictive models were valid to predict the process outcomes

Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations

Metabolomics: phenotype-driven modules in a multifluid metabolic map Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field