Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities
Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few charac...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Alan Hubbard [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © COPYRIGHT 2015 National Academy of Sciences |
|---|
| Übergeordnetes Werk: |
Enthalten in: Proceedings of the National Academy of Sciences of the United States of America - Washington, DC : NAS, 1877, 112(2015), 32, Seite E4438 |
|---|---|
| Übergeordnetes Werk: |
volume:112 ; year:2015 ; number:32 ; pages:E4438 |
| Links: |
|---|
| DOI / URN: |
10.1073/pnas.1501705112 |
|---|
| Katalog-ID: |
OLC1970280425 |
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| 520 | |a Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. | ||
| 540 | |a Nutzungsrecht: © COPYRIGHT 2015 National Academy of Sciences | ||
| 650 | 4 | |a Antibodies, Protozoan - immunology | |
| 650 | 4 | |a Biological Markers - blood | |
| 650 | 4 | |a Malaria, Falciparum - blood | |
| 650 | 4 | |a Malaria, Falciparum - parasitology | |
| 650 | 4 | |a Plasmodium falciparum - physiology | |
| 650 | 4 | |a Antibody Specificity - immunology | |
| 650 | 4 | |a Antibody Formation - immunology | |
| 650 | 4 | |a Malaria, Falciparum - epidemiology | |
| 650 | 4 | |a Plasmodium falciparum - immunology | |
| 650 | 4 | |a Antigens, Protozoan - immunology | |
| 650 | 4 | |a Malaria, Falciparum - immunology | |
| 650 | 4 | |a Plasmodium falciparum - genetics | |
| 650 | 4 | |a Host-parasite relationships | |
| 650 | 4 | |a Observations | |
| 650 | 4 | |a Plasmodium falciparum | |
| 650 | 4 | |a Health aspects | |
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| 700 | 0 | |a Peter D. Crompton |4 oth | |
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| 700 | 0 | |a Moses R. Kamya |4 oth | |
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10.1073/pnas.1501705112 doi PQ20160211 (DE-627)OLC1970280425 (DE-599)GBVOLC1970280425 (PRQ)g2115-53398866f032c879cf5686496cf248c74d81710813394c2399e4a2cc308ac9590 (KEY)0583363920150000112003204438novelserologicbiomarkersprovideaccurateestimatesof DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Alan Hubbard verfasserin aut Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. Nutzungsrecht: © COPYRIGHT 2015 National Academy of Sciences Antibodies, Protozoan - immunology Biological Markers - blood Malaria, Falciparum - blood Malaria, Falciparum - parasitology Plasmodium falciparum - physiology Antibody Specificity - immunology Antibody Formation - immunology Malaria, Falciparum - epidemiology Plasmodium falciparum - immunology Antigens, Protozoan - immunology Malaria, Falciparum - immunology Plasmodium falciparum - genetics Host-parasite relationships Observations Plasmodium falciparum Health aspects Intervention Proteins Parasitic protozoa Malaria Antigens Kinetics Jeff Skinner oth Emmanuel Arinaitwe oth David L. Smith oth Christopher J. Drakeley oth Harriet Mayanja-Kizza oth Grant Dorsey oth Danica A. Helb oth Peter D. Crompton oth Isaac Ssewanyana oth Bryan Greenhouse oth Philip L. Felgner oth Philip J. Rosenthal oth Kevin K. A. Tetteh oth Jordan Tappero oth James G. Beeson oth Moses R. Kamya oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 112(2015), 32, Seite E4438 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:112 year:2015 number:32 pages:E4438 http://dx.doi.org/10.1073/pnas.1501705112 Volltext http://www.pnas.org/content/112/32/E4438.abstract http://www.ncbi.nlm.nih.gov/pubmed/26216993 http://search.proquest.com/docview/1704124453 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 112 2015 32 E4438 |
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10.1073/pnas.1501705112 doi PQ20160211 (DE-627)OLC1970280425 (DE-599)GBVOLC1970280425 (PRQ)g2115-53398866f032c879cf5686496cf248c74d81710813394c2399e4a2cc308ac9590 (KEY)0583363920150000112003204438novelserologicbiomarkersprovideaccurateestimatesof DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Alan Hubbard verfasserin aut Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. Nutzungsrecht: © COPYRIGHT 2015 National Academy of Sciences Antibodies, Protozoan - immunology Biological Markers - blood Malaria, Falciparum - blood Malaria, Falciparum - parasitology Plasmodium falciparum - physiology Antibody Specificity - immunology Antibody Formation - immunology Malaria, Falciparum - epidemiology Plasmodium falciparum - immunology Antigens, Protozoan - immunology Malaria, Falciparum - immunology Plasmodium falciparum - genetics Host-parasite relationships Observations Plasmodium falciparum Health aspects Intervention Proteins Parasitic protozoa Malaria Antigens Kinetics Jeff Skinner oth Emmanuel Arinaitwe oth David L. Smith oth Christopher J. Drakeley oth Harriet Mayanja-Kizza oth Grant Dorsey oth Danica A. Helb oth Peter D. Crompton oth Isaac Ssewanyana oth Bryan Greenhouse oth Philip L. Felgner oth Philip J. Rosenthal oth Kevin K. A. Tetteh oth Jordan Tappero oth James G. Beeson oth Moses R. Kamya oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 112(2015), 32, Seite E4438 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:112 year:2015 number:32 pages:E4438 http://dx.doi.org/10.1073/pnas.1501705112 Volltext http://www.pnas.org/content/112/32/E4438.abstract http://www.ncbi.nlm.nih.gov/pubmed/26216993 http://search.proquest.com/docview/1704124453 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 112 2015 32 E4438 |
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10.1073/pnas.1501705112 doi PQ20160211 (DE-627)OLC1970280425 (DE-599)GBVOLC1970280425 (PRQ)g2115-53398866f032c879cf5686496cf248c74d81710813394c2399e4a2cc308ac9590 (KEY)0583363920150000112003204438novelserologicbiomarkersprovideaccurateestimatesof DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Alan Hubbard verfasserin aut Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. Nutzungsrecht: © COPYRIGHT 2015 National Academy of Sciences Antibodies, Protozoan - immunology Biological Markers - blood Malaria, Falciparum - blood Malaria, Falciparum - parasitology Plasmodium falciparum - physiology Antibody Specificity - immunology Antibody Formation - immunology Malaria, Falciparum - epidemiology Plasmodium falciparum - immunology Antigens, Protozoan - immunology Malaria, Falciparum - immunology Plasmodium falciparum - genetics Host-parasite relationships Observations Plasmodium falciparum Health aspects Intervention Proteins Parasitic protozoa Malaria Antigens Kinetics Jeff Skinner oth Emmanuel Arinaitwe oth David L. Smith oth Christopher J. Drakeley oth Harriet Mayanja-Kizza oth Grant Dorsey oth Danica A. Helb oth Peter D. Crompton oth Isaac Ssewanyana oth Bryan Greenhouse oth Philip L. Felgner oth Philip J. Rosenthal oth Kevin K. A. Tetteh oth Jordan Tappero oth James G. Beeson oth Moses R. Kamya oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 112(2015), 32, Seite E4438 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:112 year:2015 number:32 pages:E4438 http://dx.doi.org/10.1073/pnas.1501705112 Volltext http://www.pnas.org/content/112/32/E4438.abstract http://www.ncbi.nlm.nih.gov/pubmed/26216993 http://search.proquest.com/docview/1704124453 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 112 2015 32 E4438 |
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10.1073/pnas.1501705112 doi PQ20160211 (DE-627)OLC1970280425 (DE-599)GBVOLC1970280425 (PRQ)g2115-53398866f032c879cf5686496cf248c74d81710813394c2399e4a2cc308ac9590 (KEY)0583363920150000112003204438novelserologicbiomarkersprovideaccurateestimatesof DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Alan Hubbard verfasserin aut Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. Nutzungsrecht: © COPYRIGHT 2015 National Academy of Sciences Antibodies, Protozoan - immunology Biological Markers - blood Malaria, Falciparum - blood Malaria, Falciparum - parasitology Plasmodium falciparum - physiology Antibody Specificity - immunology Antibody Formation - immunology Malaria, Falciparum - epidemiology Plasmodium falciparum - immunology Antigens, Protozoan - immunology Malaria, Falciparum - immunology Plasmodium falciparum - genetics Host-parasite relationships Observations Plasmodium falciparum Health aspects Intervention Proteins Parasitic protozoa Malaria Antigens Kinetics Jeff Skinner oth Emmanuel Arinaitwe oth David L. Smith oth Christopher J. Drakeley oth Harriet Mayanja-Kizza oth Grant Dorsey oth Danica A. Helb oth Peter D. Crompton oth Isaac Ssewanyana oth Bryan Greenhouse oth Philip L. Felgner oth Philip J. Rosenthal oth Kevin K. A. Tetteh oth Jordan Tappero oth James G. Beeson oth Moses R. Kamya oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 112(2015), 32, Seite E4438 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:112 year:2015 number:32 pages:E4438 http://dx.doi.org/10.1073/pnas.1501705112 Volltext http://www.pnas.org/content/112/32/E4438.abstract http://www.ncbi.nlm.nih.gov/pubmed/26216993 http://search.proquest.com/docview/1704124453 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 112 2015 32 E4438 |
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Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities |
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Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. |
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Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. |
| abstract_unstemmed |
Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1970280425</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714175944.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160211s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1073/pnas.1501705112</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160211</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1970280425</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1970280425</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)g2115-53398866f032c879cf5686496cf248c74d81710813394c2399e4a2cc308ac9590</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0583363920150000112003204438novelserologicbiomarkersprovideaccurateestimatesof</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">500</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Alan Hubbard</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © COPYRIGHT 2015 National Academy of Sciences</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Antibodies, Protozoan - immunology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biological Markers - blood</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Malaria, Falciparum - blood</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Malaria, Falciparum - parasitology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasmodium falciparum - physiology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Antibody Specificity - immunology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Antibody Formation - immunology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Malaria, Falciparum - epidemiology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasmodium falciparum - immunology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Antigens, Protozoan - immunology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Malaria, Falciparum - immunology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasmodium falciparum - genetics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Host-parasite relationships</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Observations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasmodium falciparum</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Health aspects</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intervention</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Proteins</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Parasitic protozoa</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Malaria</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Antigens</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kinetics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jeff Skinner</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Emmanuel Arinaitwe</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">David L. 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