Distinct Metabolic Profile Associated with a Fatal Outcome in COVID-19 Patients during the Early Epidemic in Italy
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Abstract
Understanding the metabolic alterations occurring during an infection is a key element for identifying potential indicators of the disease prognosis, which are fundamental for developing efficient diagnostic tools and offering the best therapeutic treatment to the patient. Here, exploiting high-throughput metabolomics data, we identified the first metabolic profile associated with a fatal outcome, not correlated with preexisting clinical conditions or the oxygen demand at the moment of diagnosis.
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SciScore for 10.1101/2021.04.13.21255117: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources R package LIMMA was applied for differential abundance analysis between different mask types (Nasal cannula/VentMask/CPAP), outcome (survivors/non-survivors), and severity (moderate/severe). LIMMAsuggested: (LIMMA, RRID:SCR_010943)Heatmap was built using the R package ComplexHeatmap. ComplexHeatmapsuggested: (ComplexHeatmap, RRID:SCR_017270)For each community large enough (n>30), metabolite set enrichment analysis (MSEA) with KEGG and Metabolon terms via the Python module gseapy … SciScore for 10.1101/2021.04.13.21255117: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources R package LIMMA was applied for differential abundance analysis between different mask types (Nasal cannula/VentMask/CPAP), outcome (survivors/non-survivors), and severity (moderate/severe). LIMMAsuggested: (LIMMA, RRID:SCR_010943)Heatmap was built using the R package ComplexHeatmap. ComplexHeatmapsuggested: (ComplexHeatmap, RRID:SCR_017270)For each community large enough (n>30), metabolite set enrichment analysis (MSEA) with KEGG and Metabolon terms via the Python module gseapy was performed. KEGGsuggested: (KEGG, RRID:SCR_012773)Pythonsuggested: (IPython, RRID:SCR_001658)The final network was build using Cytoscape and biomarkers that were significantly associated with death were highlighted. Cytoscapesuggested: (Cytoscape, RRID:SCR_003032)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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