S3 Data - Alterations in the serum metabolome in patients with the COVID-19 Omicron variant and in recovered cases by Feng Gao (3548)

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Clinical characteristics of COVID-19 cases and healthy controls in the discovery cohort. by Feng Gao (3548)

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Baseline characteristics of mother–infant pairs by child mortality status (N = 11,519). by Samuel Sendagala (21417164)

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Child mortality rate per 1,000 person-years during the 18-month follow-up period. by Samuel Sendagala (21417164)

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S7 Data - Alterations in the serum metabolome in patients with the COVID-19 Omicron variant and in recovered cases by Feng Gao (3548)

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S2 Data - Alterations in the serum metabolome in patients with the COVID-19 Omicron variant and in recovered cases by Feng Gao (3548)

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Relative intensities of differential metabolites that may serve as potential metabolic biomarkers in Omicron variant cases. by Feng Gao (3548)

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S4 Data - Alterations in the serum metabolome in patients with the COVID-19 Omicron variant and in recovered cases by Feng Gao (3548)

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S6 Data - Alterations in the serum metabolome in patients with the COVID-19 Omicron variant and in recovered cases by Feng Gao (3548)

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S5 Data - Alterations in the serum metabolome in patients with the COVID-19 Omicron variant and in recovered cases by Feng Gao (3548)

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ROC curve analysis for the predictive power of biomarkers for distinguishing between patients with Omicron variant and healthy controls. by Feng Gao (3548)

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Analysis of the correlation between pharyngeal microbiomics, metabolomics and clinical indicators between OC and HC. by Feng Gao (3548)

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Kaplan–Meier cumulative survival curve for infants exposed and not exposed to HIV. by Samuel Sendagala (21417164)

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Metabolome KEGG enrichment analysis of serum in Omicron variant cases and recovered cases. by Feng Gao (3548)

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Mortality dataset used in analysis. by Samuel Sendagala (21417164)

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Untargeted metabolomic profiling of sera from Omicron variant cases of the discovery cohort. by Feng Gao (3548)

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S8 Data - Alterations in the serum metabolome in patients with the COVID-19 Omicron variant and in recovered cases by Feng Gao (3548)

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Average % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for brown non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for brown non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked. by Indrani Bera (804948)

“…<p>Average of % peptides counts for different classes of proteins at different germination time points and significant p-value indicated as compared to soaked sample (*p< 0.05, **p<0.01, ***p<0.001) for garbanzo non-trypsinised with shades of green showing increase and red showing decrease with respect to soaked.…”

Path diagram of the 2-factor CFA analysis. by Vincenza Tarantino (182506)

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