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Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a… — Europe PMC PMID:32598393

Source: Europe PMC (en)
Original: https://europepmc.org/article/MED/32598393
DOI: 10.1371/journal.pntd.0008407
License (as archived): CC0-1.0
Archived: 2026-11-08T08:10:08.765Z
SHA-256 of the archived text: 3dcf9393cbc189732070c8ce65aca412612b4f205bbf7af81d780597b0c9f5b8
Stable URL of this page: https://metraipolis.com/documento/dc_e69080a63bf1

How to cite

Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a… — Europe PMC PMID:32598393 — Europe PMC (en), https://europepmc.org/article/MED/32598393 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e69080a63bf1 (Metraipolis archive dc_e69080a63bf1, sha256 3dcf9393cbc189732070c8ce65aca412612b4f205bbf7af81d780597b0c9f5b8)

BibTeX

@misc{dce69080a63bf1,
  title = {Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a… — Europe PMC PMID:32598393},
  howpublished = {Europe PMC (en)},
  url = {https://europepmc.org/article/MED/32598393},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e69080a63bf1; sha256 3dcf9393cbc189732070c8ce65aca412612b4f205bbf7af81d780597b0c9f5b8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: Europe PMC (en), https://europepmc.org/article/MED/32598393. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Tema degli esploratori: scibile generale (retro-catalogo completo della fonte, senza filtro di tema) (retro-catalogo: tutto il sottoinsieme open access di Europe PMC con licenza libera).
Pubblicazione: PLoS neglected tropical diseases, 2020 (prima pubblicazione 2020-06-29).
Identificativi: PMID 32598393 · PMCID PMC7351238 · DOI 10.1371/journal.pntd.0008407.
Pagina: https://europepmc.org/article/MED/32598393 — https://doi.org/10.1371/journal.pntd.0008407. Autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza dell'articolo dichiarata da Europe PMC: CC0 (la versione è indicata dall'editore sulla pagina). Abstract riportato senza modifiche al contenuto, con attribuzione; deposito marcato CC0.
Letto il 2026-11-08T08:04:41.347Z tramite l'API REST ufficiale di Europe PMC (EMBL-EBI), nel rispetto del robots.txt e del crawl-delay.