Evidence map›Paper›PMID 39861837›Full record

ArticleViruses2024

Modeling BK Virus Infection in Renal Transplant Recipients.

Nicholas Myers, Dana Droz, Bruce W Rogers, Hien Tran, Kevin B Flores, Cliburn Chan, Stuart J Knechtle, Annette M Jackson, Xunrong Luo, Eileen T Chambers and 1 more

Abstract read
In one paragraph

Article in Viruses, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Nicholas MyersCenter for Research in Scientific Computation, Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA.ORCID 0009-0006-6522-4947
Dana DrozCenter for Research in Scientific Computation, Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA.
Bruce W RogersDepartment of Surgery, Duke University, Durham, NC 27710, USA.
Hien TranCenter for Research in Scientific Computation, Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA.ORCID 0000-0002-2348-746X
Kevin B FloresCenter for Research in Scientific Computation, Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA.
Cliburn ChanDuke Center for Human Systems Immunology, Duke University, Durham, NC 27701, USA.ORCID 0000-0001-5901-6806
Stuart J KnechtleDepartment of Surgery, Duke University, Durham, NC 27710, USA.
Annette M JacksonDepartment of Surgery, Duke University, Durham, NC 27710, USA.ORCID 0000-0003-2648-2944
Xunrong LuoDepartment of Medicine, Duke University, Durham, NC 27710, USA.
Eileen T ChambersDepartment of Surgery, Duke University, Durham, NC 27710, USA.
Janice M McCarthyDuke Center for Human Systems Immunology, Duke University, Durham, NC 27701, USA.

Funding

Mathematical modeling for optimal control of BK virus infection in kidney transplant recipientsR21AI169170 · NIAID · DUKE UNIVERSITY · PI MCCARTHY, JANICE MARIE, TRAN, HIEN T · 2023 to 2024
$349k
NIAID NIH HHS R21 AI169170NIH HHS 1R21AI169170-01A1
6 · The paper itself

Abstract

Kidney transplant recipients require a lifelong protocol of immunosuppressive therapy to prevent graft rejection. However, these same medications leave them susceptible to opportunistic infections. One pathogen of particular concern is human polyomavirus 1, also known as BK virus (BKPyV). This virus attacks kidney tubule epithelial cells and is a direct threat to the health of the graft. Current standard of care in BK virus-infected transplant recipients is reduction in immunosuppressant therapy, to allow the patient's immune system to control the virus. This requires a delicate balance; immune suppression must be strong enough to prevent rejection, yet weak enough to allow viral clearance. We seek to model viral and immune dynamics with the ultimate goal of applying optimal control methods to this problem. In this paper, we begin with a previously published model and make simplifying assumptions that reduce the number of parameters from 20 to 14. We calibrate our model using newly available patient data and a detailed sensitivity analysis. Numerical results for multiple patients are given to show that the newer model reflects observed dynamics well.

Indexed as

BK VirusKidney TransplantationPolyomavirus InfectionsTransplant RecipientsTumor Virus InfectionsHumansImmunosuppressive AgentsImmunosuppressive AgentsBKPyVimmunosuppressionkidneymodel calibrationmodelingrenalsensitivitytransplant

Identifiers

PMID39861837
PMCPMC11768487

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.