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Record W7011747664

NIH funds multi-university study of new public health threat

2016· article· en· W7011747664 on OpenAlexaboutno aff

Bibliographic record

VenueUND Scholarly Commons (University of North Dakota) · 2016
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationPublic healthLyme diseaseDisease
DOInot available

Abstract

fetched live from OpenAlex

GRAND FORKS, N.D.—A distantly related cousin to the bacterium that causes Lyme disease is the focus of a new multi-university research study funded by the National Institutes of Health Institute of Allergy and Infectious Diseases. The grant brings together an expert team of microbiologists and tick researchers led by Principal Investigator Brian Stevenson, PhD, a professor at the University of Kentucky, and Coinvestigators Catherine A. Brissette , PhD, an assistant professor at the University of North Dakota School of Medicine and Health Sciences; and Jean Tsao, PhD, an associate professor at Michigan State University. Lyme disease is caused by a spiral-shaped bacterium known as Borrelia burgdorferi, which is the cause of more than 90 percent of all tick-borne diseases affecting humans in the United States. Estimates from the Centers for Disease Control and Prevention (CDC) suggest that 300,000 people each year are affected by Lyme disease. Lyme disease is a debilitating and significant public health problem that can result in arthritis, heart problems, and neurological impairment and disability. The focus of the new NIH grant is the Lyme disease bacterium’s distant cousin known as Borrelia miyamotoi (B. miyamotoi) ; it was first identified in ticks from Japan. People infected with B. miyamotoi may require hospitalization, with symptoms that include high fever, joint and muscle pain, and inflammation of the membranes that surround the spinal cord and the brain. Both bacteria are spread by black-legged, or deer, ticks, which are found in eastern North Dakota, eastern Manitoba, Minnesota, and Wisconsin. For deer ticks to acquire either type of bacteria, they must feed on the blood of an infected animal for several days. After which, the ticks can bite and then spread the disease to humans. However, Borrelia miyamotoi may be the more insidious of the two bacterial cousins. In addition to infecting ticks that feed on infected animals, B. miyamotoi can also be passed transovarially, that is, from the tick mother to its offspring by infecting the eggs in the tick’s ovaries—significantly multiplying the number of ticks that can spread the infection. But almost nothing is known about how or where in the tick the bacterium takes hold. “We are excited to be part of a dynamic research team on a newly recognized human pathogen,” Brissette said. “The first reports of Borrelia miyamotoi infection are only from 2011 in Russia, and we know almost nothing about this bacterium. For a microbiologist, there’s nothing more thrilling than being on the front lines of discovery.” Other researchers working on the study are PhD candidates Christina Savage, in the Stevenson lab; Brandee Stone, MS, in the Brissette lab; and Seungeun Han, DVM, in the Tsao lab. They will assist the principal and coinvestigators in a series of synergistic studies to expand the understanding of B. miyamotoi transmission and infection mechanisms, including where the bacterium resides within the tick, how fast the bacterium is transmitted once a tick begins feeding, how the bacterium evades host immune responses, and how transovarial transmission of B. miyamotoi affects human disease risk. “These results will help us understand the threat of this emerging pathogen to humans,” Brissette said.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.056
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.000
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0560.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.110
GPT teacher head0.296
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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