Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

Abstract Introduction The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. Objectives Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient’s infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). Methods High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. Results The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74–0.91) and 0.76 (CI 0.67–0.86). Conclusion Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Download Full-text

Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome

10.1101/2020.12.09.20246389 ◽

2020 ◽

Author(s):

Ivayla Roberts ◽

Marina Wright Muelas ◽

Joseph M. Taylor ◽

Andrew S. Davison ◽

Yun Xu ◽

...

Keyword(s):

Statistical Significance ◽

Untargeted Metabolomics ◽

Patient Treatment ◽

Intermediary Metabolites ◽

Qualitative Detection ◽

Prognostic Tests ◽

Multiple Predictor ◽

Negative Ionization ◽

Bayesian Logistic Regression ◽

Host Inflammatory Response

AbstractThe diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, existing metabolomics studies are either underpowered, measure only a restricted subset of metabolites (‘targeted metabolomics’), compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model.We here provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict patient’s infection severity (i.e. mild or severe) and potential outcome (i.e. discharged or deceased).High resolution untargeted LC-MS/MS analysis was performed on patient serum using both positive and negative ionization. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model. The predictors were selected for their relevant biological function and include cytosine (reflecting viral load), kynurenine (reflecting host inflammatory response), nicotinuric acid, and multiple short chain acylcarnitines (energy metabolism) among others.Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.

Download Full-text

Randomized and non-randomized evidence for the effect of compulsory community and involuntary out-patient treatment on health service use: systematic review and meta-analysis

Psychological Medicine ◽

10.1017/s0033291706008592 ◽

2006 ◽

Vol 37 (1) ◽

pp. 3-14 ◽

Cited By ~ 43

Author(s):

STEPHEN KISELY ◽

LESLIE ANNE CAMPBELL ◽

ANITA SCOTT ◽

NEIL J. PRESTON ◽

JIANGUO XIAO

Keyword(s):

Systematic Review ◽

Service Use ◽

Hospital Admissions ◽

Meta Analysis ◽

Statistical Significance ◽

Community Treatment ◽

Patient Treatment ◽

Wide Range ◽

Compulsory Community Treatment ◽

Bed Days

Background. There is limited randomized controlled trial (RCT) evidence for compulsory community treatment. Other study methods may clarify their effectiveness. We reviewed RCT and non-RCT evidence for the effect of compulsory community treatment on hospital admissions, bed-days, compliance and out-patient contacts.Method. A systematic review of RCTs, controlled before-and-after (CBA) studies, and interrupted time series (ITS) analyses. Meta-analysis of RCTs.Results. Eight papers covering five studies (two RCTs and three CBAs) met inclusion criteria (total n=1108). There was no statistical difference in 12-month admission rates between subjects on involuntary out-patient treatment and controls. Survival analyses of time to admission were equivocal. All five studies reported decreases in the number of bed-days following involuntary out-patient treatment but this only reached statistical significance in one situation; patients receiving the intervention were less likely to have admissions of over 100 days. There was no difference in treatment adherence between the intervention and control groups in either RCT or two of the CBA studies. However, the third CBA study reported a statistically significant increase of nearly five visits in the mean number of overall contacts in the involuntary out-patient treatment group.Conclusions. The evidence for involuntary out-patient treatment in reducing either admissions or bed-days is very limited. It therefore cannot be seen as a less restrictive alternative to admission. Other effects are uncertain. Evaluation of a wide range of outcomes should be included if this type of legislation is introduced.

Download Full-text

High-Resolution UPLC-MS Profiling of Anthocyanins and Flavonols of Red Cabbage (Brassica oleracea L. var. capitata f. rubra DC.) Cultivated in Egypt and Evaluation of Their Biological Activity

Molecules ◽

10.3390/molecules26247567 ◽

2021 ◽

Vol 26 (24) ◽

pp. 7567

Author(s):

Khaled Ahmed Mansour ◽

Sherifa Fahmy Moustafa ◽

Soad Mohamed Abdelkhalik

Keyword(s):

High Resolution ◽

Brassica Oleracea ◽

Methanolic Extract ◽

Statistical Significance ◽

Anticancer Activities ◽

Red Cabbage ◽

Ionization Mode ◽

Negative Ionization ◽

First Time ◽

Brassica Oleracea L

In this paper, biological investigations and a high-resolution UPLC-PDA-ESI-qTOF-HRMS technique were employed for Brassica oleracea L. var. capitata f. rubra DC. (red cabbage) of the family Brassicaceae (Cruciferae), cultivated in Egypt, for the first time. The positive ionization mode is usually performed to identify anthocyanins. However, this technique cannot differentiate between anthocyanins and corresponding non-anthocyanin polyphenols. Thus, the negative ionization mode was also used, as it provided a series of characteristic ions for the MS analysis of anthocyanins. This helped in identifying five kaempferol derivatives for the first time in red cabbage, as well as nine—previously reported—anthocyanins. For the biological investigations, the acidified methanolic extract of fresh leaves and the methanolic extract of air-dried powdered leaves were examined for their antioxidant, antimicrobial, and anticancer activities. The freshly prepared phenolic extract was proven to be more biologically potent. Statistical significance was determined for its anticancer activity in comparison with standard doxorubicin.

Download Full-text

From mass spectral features to molecules in molecular networks: a novel workflow for untargeted metabolomics.

10.1101/2021.12.21.473622 ◽

2021 ◽

Author(s):

Damien Olivier-Jimenez ◽

Zakaria Bouchouireb ◽

Simon Ollivier ◽

Julia Mocquard ◽

Pierre-Marie Allard ◽

...

Keyword(s):

Similarity Score ◽

Untargeted Metabolomics ◽

Molecular Networks ◽

Exact Mass ◽

Mass Spectral ◽

Calculated Mass ◽

Single Compound ◽

Molecular Masses ◽

Negative Ionization ◽

Ion Species

In the context of untargeted metabolomics, molecular networking is a popular and efficient tool which organizes and simplifies mass spectrometry fragmentation data (LC-MS/MS), by clustering ions based on a cosine similarity score. However, the nature of the ion species is rarely taken into account, causing redundancy as a single compound may be present in different forms throughout the network. Taking advantage of the presence of such redundant ions, we developed a new method named MolNotator. Using the different ion species produced by a molecule during ionization (adducts, dimers, trimers, in-source fragments), a predicted molecule node (or neutral node) is created by triangulation, and ultimately computing the associated molecule calculated mass. These neutral nodes provide researchers with several advantages. Firstly, each molecule is then represented in its ionization context, connected to all produced ions and indirectly to some coeluted compounds, thereby also highlighting unexpected widely present adduct species. Secondly, the predicted neutrals serve as anchors to merge the complementary positive and negative ionization modes into a single network. Lastly, the dereplication is improved by the use of all available ions connected to the neutral nodes, and the computed molecular masses can be used for exact mass dereplication. MolNotator is available as a Python library and was validated using the lichen database spectra acquired on an Orbitrap, computing neutral molecules for >90% of the 156 molecules in the dataset. By focusing on actual molecules instead of ions, MolNotator greatly facilitates the selection of molecules of interest.

Download Full-text

Assesment of the Quality and Reliability of the Information on Bone Tumor on Youtube (Preprint)

10.2196/preprints.21793 ◽

2020 ◽

Author(s):

Sezgin Bahadır Tekin ◽

Erman Öğümsöğütlü

Keyword(s):

Bone Tumors ◽

Statistical Significance ◽

Treatment Compliance ◽

Scoring Systems ◽

Patient Treatment ◽

Accurate Information ◽

Content Quality ◽

Global Quality ◽

Youtube Videos ◽

Quality And Reliability

UNSTRUCTURED Introduction: Our aim is to assess the content quality and reliability of YouTube videos on bone tumors. Methods: We searched YouTube using the keyword “bone tumors,” and included in our study the first 50 videos listed. Two orthopedic surgeons analyzed the videos, and then, we examined them in terms of length, number of views and likes, and source. To evaluate their content quality, the Global Quality Score (GQS) (0–4), Journal of the American Medical Association (JAMA) (0–5), and DISCERN (15–75) scoring systems were used; based on these, the obtained data were statistically analyzed. Results: Of the 50 videos analyzed, four (8%) included animation, while 46 (92%) did not; 12 (24%) were uploaded by doctors, 23 (46%) by health channels, 10 (20%) by universities, three (6%) by clinics, and one (2%) each by a hospital and a trainer. The average video length was 16 minutes 18 seconds (38-6088 seconds), average number of views 14,856.24, and average number of likes 130.50 (1-1448). The mean scores of GQS, JAMA, and DISCERN were 2.22 (1-4), 2.12 (1-3), and 33.48 (15-75), respectively. There was no statistical significance in the scores and video length, or the number of likes and views between the two researchers. Discussion: YouTube videos on bone tumors have low content quality. Improvement of the same will help dispense correct information to patients, so that they can continue their treatment. We believe that patient treatment compliance can be increased by accelerating the patient’s preparation and adaptation process for treatment with accurate information.

Download Full-text

The analysis of the effectiveness and safety of levofloxacin in the treatment of lower respiratory tract infection

International Current Pharmaceutical Journal ◽

10.3329/icpj.v4i12.25598 ◽

2015 ◽

Vol 4 (12) ◽

pp. 462-464

Author(s):

Ibrahim Dughaim Alreshidi ◽

Khalid Mohammed Al-Mutairi ◽

Xie Han

Keyword(s):

Tract Infection ◽

Respiratory Tract ◽

Respiratory Tract Infection ◽

Lower Respiratory Tract Infection ◽

Lower Respiratory Tract ◽

Statistical Significance ◽

Patient Treatment ◽

Control Group ◽

High Dose ◽

Observation Group

Objective of the current study was to investigate the effectiveness and safety of levofloxacin in the treatment of lower respiratory tract infection. The study was carried out in hospital on 120 patients with lower respiratory tract infection from January 2014 to January 2015. Patients were randomly divided into control and observation groups, each group contained 60 patients. The control group was given the routine dose of levofloxacin, whereas the observation group received the high dose of levofloxacin. The clinical effectiveness and incidence rate of untoward reactions between the two groups were statistically analyzed and evaluated. Patients cure rate in the observation group was 53.33%, significantly higher than that of the control group which was only 36.67%. Their differences have statistical significance (P<0.05). Observation group demonstrated a very good total effective rate of 93.33%, compared to the control group (78.33%). Their differences have statistical significance (P<0.05). Incidence of adverse reactions in case of both the observation and control group patients, were relatively low, resulting insignificant statistical difference between the groups (P>0.05). This study shows better clinical curative effect of high doses of levofloxacin treating lower respiratory infection with minimum risk. This method, which can significantly improve the quality of patient treatment with low adverse reaction risk, is worth popularizing in clinical use.Alreshidi et al., International Current Pharmaceutical Journal, November 2015, 4(12): 462-464

Download Full-text

Untargeted metabolomics and transcriptomics identified glutathione metabolism disturbance and PCS and TMAO as potential biomarkers for ER stress in lung

Scientific Reports ◽

10.1038/s41598-021-92779-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zijing Wang ◽

Peng Ma ◽

Yisa Wang ◽

Biyu Hou ◽

Can Zhou ◽

...

Keyword(s):

Er Stress ◽

Statistical Significance ◽

Redox Homeostasis ◽

Untargeted Metabolomics ◽

Glutathione Metabolism ◽

Critical Pathways ◽

Chronic Lung Diseases ◽

Lung Infections ◽

Mouse Lungs ◽

Altered Pathway

AbstractEndoplasmic reticulum (ER) stress is a cellular state that results from the overload of unfolded/misfolded protein in the ER that, if not resolved properly, can lead to cell death. Both acute lung infections and chronic lung diseases have been found related to ER stress. Yet no study has been presented integrating metabolomic and transcriptomic data from total lung in interpreting the pathogenic state of ER stress. Total mouse lungs were used to perform LC–MS and RNA sequencing in relevance to ER stress. Untargeted metabolomics revealed 16 metabolites of aberrant levels with statistical significance while transcriptomics revealed 1593 genes abnormally expressed. Enrichment results demonstrated the injury ER stress inflicted upon lung through the alteration of multiple critical pathways involving energy expenditure, signal transduction, and redox homeostasis. Ultimately, we have presented p-cresol sulfate (PCS) and trimethylamine N-oxide (TMAO) as two potential ER stress biomarkers. Glutathione metabolism stood out in both omics as a notably altered pathway that believed to take important roles in maintaining the redox homeostasis in the cells critical for the development and relief of ER stress, in consistence with the existing reports.

Download Full-text

Imaging molecules adsorbed onto electrodes under potential control by scanning probe microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100086180 ◽

1991 ◽

Vol 49 ◽

pp. 376-377 ◽

Cited By ~ 1

Author(s):

N.J. Tao ◽

J.A. DeRose ◽

P.I. Oden ◽

S.M. Lindsay

Keyword(s):

Surface Charge ◽

Environmental Effects ◽

Scanning Probe Microscopy ◽

Statistical Significance ◽

Electrochemical Methods ◽

Surface Structures ◽

Scanning Probe ◽

Potential Control ◽

Afm Imaging ◽

Special Circumstances

Clemmer and Beebe have pointed out that surface structures on graphite substrates can be misinterpreted as biopolymer images in STM experiments. We have been using electrochemical methods to react DNA fragments onto gold electrodes for STM and AFM imaging. The adsorbates produced in this way are only homogeneous in special circumstances. Searching an inhomogeneous substrate for ‘desired’ images limits the value of the data. Here, we report on a reversible method for imaging adsorbates. The molecules can be lifted onto and off the substrate during imaging. This leaves no doubt about the validity or statistical significance of the images. Furthermore, environmental effects (such as changes in electrolyte or surface charge) can be investigated easily.

Download Full-text