Assessment of Virological Contributions to COVID-19 Outcomes in a Longitudinal Cohort of Hospitalized Adults

Article activity feed

1. Version published to 10.1093/ofid/ofac027

   Feb 14, 2022
2. 

   ScreenIT

   Jul 8, 2021

   SciScore for 10.1101/2021.07.02.21259665: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   Ethics	Consent: Specimen collection and processing: After IRB approval, individuals over the age of 18 admitted to Northwestern Memorial Hospital with a positive, PCR-based COVID-19 diagnostic test, who provided informed consent themselves or through an appropriate surrogate, were enrolled in the study.
   Sex as a biological variable	not detected.
   Randomization	not detected.
   Blinding	not detected.
   Power Analysis	not detected.

   Table 2: Resources

   Antibodies
   Sentences	Resources
   Following another three washes, plates were incubated for one hour with 100 µL/well of 1 µg/mL horseradish peroxidase conjugated goat anti-human IgG antibody, F(ab′)2 (Chemicon) in blocking buffer prior to development with …

   SciScore for 10.1101/2021.07.02.21259665: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   Ethics	Consent: Specimen collection and processing: After IRB approval, individuals over the age of 18 admitted to Northwestern Memorial Hospital with a positive, PCR-based COVID-19 diagnostic test, who provided informed consent themselves or through an appropriate surrogate, were enrolled in the study.
   Sex as a biological variable	not detected.
   Randomization	not detected.
   Blinding	not detected.
   Power Analysis	not detected.

   Table 2: Resources

   Antibodies
   Sentences	Resources
   Following another three washes, plates were incubated for one hour with 100 µL/well of 1 µg/mL horseradish peroxidase conjugated goat anti-human IgG antibody, F(ab′)2 (Chemicon) in blocking buffer prior to development with 3,3’,5,5’-Tetramethylbenzidine (TMB) solution (Fisher).	anti-human IgG suggested: None
   Anti-Spike Antibody Quantification by ELISA: Anti-Spike IgG concentration in serum samples was determined utilizing an ELISA assay as described [37, 38].	Anti-Spike IgG suggested: None
   A four-parameter logistic regression of the multi-concentration standard curve was obtained using CR3022, a recombinant human anti-SARS-CoV-RBD IgG antibody, with a known affinity to the RBD of SARS-CoV-2 (CR3022 antibody, Creative Biolabs #MRO-1214LC).	CR3022 suggested: (Imported from the IEDB Cat# CR3022, RRID:AB_2848080) anti-SARS-CoV-RBD IgG suggested: None
   Anti-HLA Antibody Quantification: The presence of HLA Class I and Class II antibodies was measured using the FlowPRA™ Screening assay (One Lambda, A Thermo Fisher Scientific Brand), following manufacturer’s recommendation.	Anti-HLA suggested: None Class II suggested: None
   Following the manufacturer’s recommended protocol, plasma samples were diluted five to ten times in Tris-Buffered Saline (TBS) containing 3% BSA (w/v) before added into the microtiter plate wells containing the PAI-1 capture antibodies.	PAI-1 capture antibodies. suggested: None
   After washing away the unbound fraction of plasma samples, the amount of PAI-1 captured in the wells was quantified after sequential binding, washing, incubation of primary antibody against PAI-1, incubation of the secondary antibody-HRP conjugate and development utilizing a TMB substrate.	PAI-1 suggested: None antibody-HRP suggested: None
   Recombinant DNA
   Sentences	Resources
   The NTD sequence was synthesized (Twist Bioscience) and cloned into pMCSG53 [35], yielding pMCSG53-N-NTD (available through BEI Resources, NR-52428).	pMCSG53 suggested: RRID:Addgene_167255) pMCSG53-N-NTD suggested: None
   The CTD sequence was synthesized (Bio Basic) and cloned into pET11a (Novagen), yielding pET11a-N-CTD (available through BEI Resources, NR-52434).	pET11a suggested: RRID:Addgene_105501) pET11a-N-CTD suggested: None
   , amino acids 1-412), pET-28a(+) containing the entire Wuhan-Hu-1 Nucleocapsid open reading frame was obtained from BEI Resources (NIAID, NIH, Catalog # NR-53507) and transformed into E. coli Rosetta-1 cells (Novagen).	pET-28a(+ ) suggested: None
   Software and Algorithms
   Sentences	Resources
   Barcode sequences were trimmed from aligned reads and consensus sequence determined using iVar v1.2.2 [33] with a minimum alignment depth of 10 reads and minimum base quality of 20, and a consensus frequency threshold of 0 (i.e. majority base as the consensus).	iVar suggested: None
   Coated plates were washed three times with 250 µL of wash buffer (1x PBS with 0.5% Tween-20) using a Thermo Fisher Wellwash™	Thermo Fisher Wellwash™ suggested: (Thermo Scientific Wellwash Wellwash, RRID:SCR_020569)
   Phylogenetic Analysis: Consensus sequences for all longitudinal samples from each participant were aligned using MAFFT v7.453 software and manually edited using MEGA v6.06.	MAFFT suggested: (MAFFT, RRID:SCR_011811) MEGA suggested: (Mega BLAST, RRID:SCR_011920)
   A Maximum Likelihood (ML) phylogeny with all consensus sequences were inferred with IQ-Tree v2.0.5 using its ModelFinder function before each analysis to estimate the nucleotide substitution model best-fitted for each dataset by means of Bayesian information criterion (BIC).	IQ-Tree suggested: (IQ-TREE, RRID:SCR_017254)
   We used QuasiRecomb [40] to perform a probabilistic inference of the viral haplotypes per gene present in each viral population.	QuasiRecomb suggested: (QuasiRecomb, RRID:SCR_008812)
   After haplotype reconstruction, we discarded sequences with less than 1% frequency of the reads to avoid including sequencing errors and we calculated the pairwise genetic distance between every haplotype and the most predominant haplotype using DistanceCalculator in biopython 1.74.	DistanceCalculator suggested: None biopython suggested: (Biopython, RRID:SCR_007173)
   To obtain the number of nonsynonymous substitutions per non-synonymous site (dN) and the number of synonymous substitutions per synonymous site (dS), we followed a similar approach using CodonSeq and cal_dn_ds with the NG86 method from biopython 1.74.	CodonSeq suggested: None
   All calculations were performed using in-house scripts in python 3.8 (available upon request).	python suggested: (IPython, RRID:SCR_001658)
   Both PCA and agglomerative hierarchical clustering were performed using FactoMineR package and factoextra was used for visualization of the clustering results.	FactoMineR suggested: (FactoMineR, RRID:SCR_014602)

   ---

   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: We detected the following sentences addressing limitations in the study:

   Despite its strengths, this study has several limitations. This is a single center study and as such our population may be different from the general population. That said, during this phase of the pandemic, we were a tertiary referral hospital that continued to accept patients from both our usual catchment area but also referrals in from other hospitals around the Chicagoland area. The study included the first wave of the pandemic and as such there was significant variability in the therapies that were given to the participants. That said, few of the participants received therapies that were subsequently found to be clinically beneficial. As such, the experience reflects the natural history of illness with general supportive measures. Nevertheless, continued exploration of the trends observed here in larger, multi-institutional cohorts is required. Lastly, the study did not include a non-hospitalized control group for comparison and utility of the identified markers to predict hospitalization or other outcomes cannot be assessed. In sum, this study found that the 4C mortality score and LDH levels at the time of admission were predictive of admission to the ICU and should be examined in larger cohorts for use in clinical risk management. Validation of a novel score based on BMI, lymphocyte count, and neutrophil count on admission may yield a useful tool for predicting outcomes of hospitalized patients. While not assessed in this study, the role of these factors and others to ...

   ---

   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.

   ---

   Results from scite Reference Check: We found no unreliable references.

   ---

   Read the original source
3. 

   Version published to 10.1101/2021.07.02.21259665v1 on medRxiv

   Jul 5, 2021