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Record W2756003490 · doi:10.1021/acs.analchem.7b02423

Mass Spectrometry-Based Chemical Cartography of a Cardiac Parasitic Infection

2017· article· en· W2756003490 on OpenAlexfundno aff
Laura‐Isobel McCall, James T. Morton, Jean Bernatchez, Jair L. Siqueira-Neto, Rob Knight, Pieter C. Dorrestein, James H. McKerrow

Bibliographic record

VenueAnalytical Chemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicTrypanosoma species research and implications
Canadian institutionsnot available
FundersDivision of Graduate EducationNational Center for Research ResourcesNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsChagas diseaseTrypanosoma cruziPathogenesisChemistryBiologyParasite hostingVirologyImmunology

Abstract

fetched live from OpenAlex

Trypanosoma cruzi parasites are the causative agents of Chagas disease, a leading infectious form of heart failure whose pathogenesis is still not fully characterized. In this work, we applied untargeted liquid chromatography-tandem mass spectrometry to heart sections from T. cruzi-infected and uninfected mice. We combined molecular networking and three-dimensional modeling to generate chemical cartographical heart models. This approach revealed for the first time preferential parasite localization to the base of the heart and regiospecific distributions of nucleoside derivatives and eicosanoids, which we correlated to tissue-damaging immune responses. We further detected novel cardiac chemical signatures related to the severity and ultimate outcome of the infection. These signatures included differential representation of higher- vs lower-molecular-weight carnitine and phosphatidylcholine family members in specific cardiac regions of mice infected with lethal or nonlethal T. cruzi strains and doses. Overall, this work provides new insights into Chagas disease pathogenesis and presents an analytical chemistry approach that can be broadly applied to the study of host-microbe interactions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.332
Teacher spread0.310 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations50
Published2017
Admission routes1
Has abstractyes

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