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LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T499","span":{"begin":678,"end":682},"obj":"Body_part"},{"id":"T500","span":{"begin":947,"end":951},"obj":"Body_part"},{"id":"T501","span":{"begin":1024,"end":1029},"obj":"Body_part"},{"id":"T502","span":{"begin":1041,"end":1043},"obj":"Body_part"},{"id":"T503","span":{"begin":1165,"end":1170},"obj":"Body_part"},{"id":"T504","span":{"begin":1191,"end":1195},"obj":"Body_part"},{"id":"T505","span":{"begin":1225,"end":1227},"obj":"Body_part"},{"id":"T506","span":{"begin":1241,"end":1246},"obj":"Body_part"},{"id":"T507","span":{"begin":1320,"end":1330},"obj":"Body_part"},{"id":"T508","span":{"begin":1525,"end":1529},"obj":"Body_part"},{"id":"T509","span":{"begin":1648,"end":1652},"obj":"Body_part"}],"attributes":[{"id":"A499","pred":"fma_id","subj":"T499","obj":"http://purl.org/sig/ont/fma/fma25056"},{"id":"A500","pred":"fma_id","subj":"T500","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A501","pred":"fma_id","subj":"T501","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A502","pred":"fma_id","subj":"T502","obj":"http://purl.org/sig/ont/fma/fma84371"},{"id":"A503","pred":"fma_id","subj":"T503","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A504","pred":"fma_id","subj":"T504","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A505","pred":"fma_id","subj":"T505","obj":"http://purl.org/sig/ont/fma/fma84371"},{"id":"A506","pred":"fma_id","subj":"T506","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A507","pred":"fma_id","subj":"T507","obj":"http://purl.org/sig/ont/fma/fma62863"},{"id":"A508","pred":"fma_id","subj":"T508","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A509","pred":"fma_id","subj":"T509","obj":"http://purl.org/sig/ont/fma/fma68646"}],"text":"An additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit."}

LitCovid-PubTator

LitCovid-PD-MONDO

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T402","span":{"begin":338,"end":346},"obj":"Disease"},{"id":"T403","span":{"begin":1041,"end":1043},"obj":"Disease"},{"id":"T404","span":{"begin":1225,"end":1227},"obj":"Disease"},{"id":"T405","span":{"begin":1387,"end":1395},"obj":"Disease"}],"attributes":[{"id":"A402","pred":"mondo_id","subj":"T402","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A403","pred":"mondo_id","subj":"T403","obj":"http://purl.obolibrary.org/obo/MONDO_0019035"},{"id":"A404","pred":"mondo_id","subj":"T404","obj":"http://purl.obolibrary.org/obo/MONDO_0019035"},{"id":"A405","pred":"mondo_id","subj":"T405","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"An additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit."}

LitCovid-PD-CLO

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T905","span":{"begin":941,"end":944},"obj":"http://purl.obolibrary.org/obo/PR_000001004"},{"id":"T906","span":{"begin":945,"end":951},"obj":"http://purl.obolibrary.org/obo/CL_0000084"},{"id":"T907","span":{"begin":952,"end":962},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T908","span":{"begin":1018,"end":1021},"obj":"http://purl.obolibrary.org/obo/CLO_0053438"},{"id":"T909","span":{"begin":1022,"end":1029},"obj":"http://purl.obolibrary.org/obo/CL_0000084"},{"id":"T910","span":{"begin":1159,"end":1162},"obj":"http://purl.obolibrary.org/obo/CLO_0053438"},{"id":"T911","span":{"begin":1163,"end":1170},"obj":"http://purl.obolibrary.org/obo/CL_0000084"},{"id":"T912","span":{"begin":1185,"end":1188},"obj":"http://purl.obolibrary.org/obo/PR_000001004"},{"id":"T913","span":{"begin":1189,"end":1195},"obj":"http://purl.obolibrary.org/obo/CL_0000084"},{"id":"T914","span":{"begin":1196,"end":1206},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T915","span":{"begin":1239,"end":1246},"obj":"http://purl.obolibrary.org/obo/CL_0000236"},{"id":"T916","span":{"begin":1331,"end":1341},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T917","span":{"begin":1412,"end":1413},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T918","span":{"begin":1498,"end":1499},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T919","span":{"begin":1523,"end":1529},"obj":"http://purl.obolibrary.org/obo/CL_0000236"},{"id":"T920","span":{"begin":1648,"end":1658},"obj":"http://purl.obolibrary.org/obo/CL_0000000"}],"text":"An additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit."}

LitCovid-PD-CHEBI

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T14749","span":{"begin":999,"end":1007},"obj":"Chemical"},{"id":"T88670","span":{"begin":1041,"end":1043},"obj":"Chemical"},{"id":"T44023","span":{"begin":1150,"end":1158},"obj":"Chemical"},{"id":"T55913","span":{"begin":1225,"end":1227},"obj":"Chemical"},{"id":"T20552","span":{"begin":1507,"end":1516},"obj":"Chemical"}],"attributes":[{"id":"A89351","pred":"chebi_id","subj":"T14749","obj":"http://purl.obolibrary.org/obo/CHEBI_35224"},{"id":"A19091","pred":"chebi_id","subj":"T88670","obj":"http://purl.obolibrary.org/obo/CHEBI_53319"},{"id":"A26484","pred":"chebi_id","subj":"T88670","obj":"http://purl.obolibrary.org/obo/CHEBI_60686"},{"id":"A8109","pred":"chebi_id","subj":"T44023","obj":"http://purl.obolibrary.org/obo/CHEBI_35224"},{"id":"A76694","pred":"chebi_id","subj":"T55913","obj":"http://purl.obolibrary.org/obo/CHEBI_53319"},{"id":"A95506","pred":"chebi_id","subj":"T55913","obj":"http://purl.obolibrary.org/obo/CHEBI_60686"},{"id":"A88463","pred":"chebi_id","subj":"T20552","obj":"http://purl.obolibrary.org/obo/CHEBI_22587"}],"text":"An additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit."}

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T120","span":{"begin":535,"end":550},"obj":"http://purl.obolibrary.org/obo/GO_0006955"},{"id":"T121","span":{"begin":945,"end":962},"obj":"http://purl.obolibrary.org/obo/GO_0042110"},{"id":"T122","span":{"begin":947,"end":962},"obj":"http://purl.obolibrary.org/obo/GO_0001775"},{"id":"T123","span":{"begin":1189,"end":1206},"obj":"http://purl.obolibrary.org/obo/GO_0042110"},{"id":"T124","span":{"begin":1191,"end":1206},"obj":"http://purl.obolibrary.org/obo/GO_0001775"},{"id":"T125","span":{"begin":1232,"end":1238},"obj":"http://purl.obolibrary.org/obo/GO_0007613"},{"id":"T126","span":{"begin":1320,"end":1341},"obj":"http://purl.obolibrary.org/obo/GO_0046649"}],"text":"An additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T342","span":{"begin":0,"end":188},"obj":"Sentence"},{"id":"T343","span":{"begin":189,"end":355},"obj":"Sentence"},{"id":"T344","span":{"begin":356,"end":581},"obj":"Sentence"},{"id":"T345","span":{"begin":582,"end":737},"obj":"Sentence"},{"id":"T346","span":{"begin":738,"end":876},"obj":"Sentence"},{"id":"T347","span":{"begin":877,"end":1288},"obj":"Sentence"},{"id":"T348","span":{"begin":1289,"end":1539},"obj":"Sentence"},{"id":"T349","span":{"begin":1540,"end":1741},"obj":"Sentence"},{"id":"T350","span":{"begin":1742,"end":1928},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"An additional major finding was the ability to connect immune features not only to disease severity at the time of sampling but also to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19 disease. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. Moreover, this UMAP embedding approach allowed us to connect these integrated immune signatures back to specific clinically measurable features of disease. The integrated immune signatures captured by Components 1 and 2 in this UMAP model provided support for the notion of Immunotypes 1 and 2. These analyses suggested that Immunotype 1, comprised of robust CD4 T cell activation, paucity of cTfh with proliferating effector/exhausted CD8 T cells and T-bet+ PB involvement, was connected to more severe disease whereas Immunotype 2, characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation and proliferating PB and memory B cells, was better captured by UMAP Component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider as patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of the tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit."}