Jonathan Schmitz

Dr. Jonathan Schmitz


Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 October 2022 in Journal of Clinical Microbiology
Verified
Journal Article
Closed access
JSJonathan Schmitz
Jonathan Schmitz
CSCharles W. Stratton
Charles W. Stratton
DPDavid H. Persing
David H. Persing
Yi-Wei TangYi-Wei Tang
Yi-Wei Tang
Nearly 40 years have elapsed since the invention of the PCR, with its extremely sensitive and specific ability to detect nucleic acids via in vitro enzyme-mediated amplification. In turn, more than 2 years have passed since the onset of the coronavirus disease 2019 (COVID-19) pandemic, during which time molecular diagnostics for infectious diseases have assumed a larger global role than ever before.

19 February 2025 in Journal of Clinical Microbiology
Verified
Journal Article
Open access
TBTomas Bermudez
Tomas Bermudez
JSJonathan Schmitz
Jonathan Schmitz
MBMalcolm Boswell
Malcolm Boswell
RHRomney Humphries
Romney Humphries
Urinary tract infections (UTIs) impose a substantial burden on patient quality of life and urine testing accounts for the majority of workload in many clinical microbiology laboratories. Traditional UTI diagnosis relies on symptoms, urinalysis, and culture which are interpreted based on historical guidelines. This approach, while foundational, presents limitations, particularly in complex cases. Low-level bacteriuria and the presence of fastidious organisms are often overlooked or entirely missed in standard urine culture, stressing the need for novel diagnostic methods and technologies. This mini-review summarizes the existing state of UTI diagnostics in 2024 and covers current and upcoming technologies including rapid molecular-based pathogen identification, next-generation sequencing, and advanced antimicrobial susceptibility testing. However, these methods represent unique challenges, and as they are implemented, they will require the field to adapt to new concepts to avoid misdiagnosis and overtreatment.

2 January 2002 in Computational and Mathematical Methods in Medicine
Verified
Journal Article
Open access
PDF file
JSJonathan Schmitz
Jonathan Schmitz
AKAnuraag R. Kansal
Anuraag R. Kansal
STSalvatore Torquato
Salvatore Torquato
We have extended an automaton model of brain tumor growth to study the effects of treatment. By varying three treatment parameters, we can simulate tumors that display clinically plausible survival times. Much of our work is dedicated to heterogeneous tumors with both treatment‐sensitive and treatment‐resistant cells. First, we investigate two‐strain systems in which resistant cells are initialized within predominantly sensitive tumors. We find that when resistant cells are not confined to a particular location, they compete more effectively with the sensitive population. Moreover, in this case, the fraction of resistant cells within the tumor is a less important indicator of patient prognosis when compared to the case in which the resistant cells are scattered throughout the tumor. In additional simulations, we investigate tumors that are initially monoclonal and treatment‐sensitive, but that undergo resistance‐mutations in response to treatment. Here, the tumors with both very frequent and very infrequent mutations develop with more spherical geometries. Tumors with intermediate mutational responses exhibit multi‐lobed geometries, as mutant strains develop at localized points on the tumors′ surfaces.

23 March 2023 in Journal of Clinical Microbiology
Verified
Journal Article
Closed access
RHRomney Humphries
Romney Humphries
EBEugene Bragin
Eugene Bragin
JPJulian Parkhill
Julian Parkhill
GMGrace Morales
Grace Morales
JSJonathan Schmitz
Jonathan Schmitz
PRPaul A. Rhodes
Paul A. Rhodes
The declining cost of performing bacterial whole-genome sequencing (WGS) coupled with the availability of large libraries of sequence data for well-characterized isolates have enabled the application of machine-learning (ML) methods to the development of nonlinear sequence-based predictive models. We tested the ML-based model developed by Next Gen Diagnostics for prediction of cefepime phenotypic susceptibility results in Escherichia coli .

15 June 2023 in Microbiology Spectrum
Verified
Journal Article
Open access
GMGrace Morales
Grace Morales
BABenjamin Abelson
Benjamin Abelson
SRSeth Reasoner
Seth Reasoner
JMJordan Miller
Jordan Miller
AEAshlee M. Earl
Ashlee M. Earl
JSJonathan Schmitz
Jonathan Schmitz
Escherichia coli is the most common etiological agent of urinary tract infections (UTIs), with UTI-associated strains designated “uropathogenic” E. coli or UPEC. Across urinary strains of E. coli , the global landscape of MGEs and its relationship to virulence factor carriage and clinical symptomatology require greater clarity.

30 November 2019 in Journal of Molecular Biology
Verified
Journal Article
Closed access
AEAllison R. Eberly
Allison R. Eberly
CBConnor J. Beebout
Connor J. Beebout
CTChing Man Carmen Tong
Ching Man Carmen Tong
GHGerald T. Van Horn
Gerald T. Van Horn
HGHamilton D. Green
Hamilton D. Green
MHMaria Hadjifrangiskou
Maria Hadjifrangiskou