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Metabo Inc rank correlation coefficient
Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
Rank Correlation Coefficient, supplied by Metabo Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rank+correlation+coefficient/pmc13066742-353-11-2?v=Metabo+Inc
Average 86 stars, based on 1 article reviews
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Article Title: A quantitative metabolic signature of host response during SARS-CoV-2 infection and recovery

Journal: iScience

doi: 10.1016/j.isci.2026.115390

Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression coefficient (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
Figure Legend Snippet: Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression coefficient (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,

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Metabo Inc rank correlation coefficient
Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
Rank Correlation Coefficient, supplied by Metabo Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rank+correlation+coefficient/pmc13066742-353-11-2?v=Metabo+Inc
Average 86 stars, based on 1 article reviews
rank correlation coefficient - by Bioz Stars, 2026-07
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
Spearman's Rank Correlation Coefficient, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rank+correlation+coefficient/pm40005526-212-13-26?v=GraphPad+Software+Inc
Average 90 stars, based on 1 article reviews
spearman's rank correlation coefficient - by Bioz Stars, 2026-07
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
Spearman's Rank Correlation Coefficient (Graphpad Prism 6 Software), supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/rank+correlation+coefficient/us12210022-184-0-6?v=GraphPad+Software+Inc
Average 90 stars, based on 1 article reviews
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MathWorks Inc latain hypercube sampling/partial rank correlation coefficient
Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression <t>coefficient</t> (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,
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Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression coefficient (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,

Journal: iScience

Article Title: A quantitative metabolic signature of host response during SARS-CoV-2 infection and recovery

doi: 10.1016/j.isci.2026.115390

Figure Lengend Snippet: Metabo-time confidently represented individualized normalization of host systemic oxidative stress and immune response during COVID-19 recovery, better than physical days since hospitalization (A) The normalization of serum reactive oxygen species levels during recovery was better captured by metabo-time than physical days since hospitalization, as evidenced by the higher R 2 statistic correlating ROS levels with metabo-time than days since hospitalization. This was quantitatively tested by joint linear modeling of ROS on both physical time and metabo-time. (B) Quantification and statistical analysis; ( n = 34), where the conditional regression coefficient (i.e., association) of physical time with ROS was null. Still, the effect of metabo-time remained highly significant, suggesting that metabo-time can fully replace physical time to characterize the individualized recovery of ROS levels. (C) Longitudinal correlation of host serum cytokine levels versus metabo-time and physical time yielded consistent findings across cytokines. (D) Cytokines identified as statistically significantly correlated with metabo-time (i.e., “normalizing” during COVID-19 recovery) were consistent with previous observations ( n = 36). , ,

Article Snippet: Correlation between Metabo-time and log-transformed CRP values was assessed using Spearman’s rank correlation coefficient.

Techniques: