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Record W3193706418 · doi:10.1186/s12931-021-01823-8

Post-treatment with the PPAR-γ agonist pioglitazone inhibits inflammation and bacterial growth during Klebsiella pneumonia

2021· letter· en· W3193706418 on OpenAlexaff
Ivan Ramirez‐Moral, Bianca Lima Ferreira, Alex F. de Vos, Tom van der Poll

Bibliographic record

VenueRespiratory Research · 2021
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsInstitute of Infection and Immunity
FundersZonMwFundação de Amparo à Pesquisa do Estado de São PauloJoint Programming Initiative on Antimicrobial Resistance
KeywordsPioglitazoneInflammationPneumoniaKlebsiella pneumoniaAgonistKlebsiella pneumoniaeMedicineBacterial pneumoniaMicrobiologyInternal medicinePharmacologyChemistryBiologyReceptorEndocrinologyEscherichia coliDiabetes mellitusType 2 diabetesBiochemistry

Abstract

fetched live from OpenAlex

Agonists of peroxisome proliferator-activated receptor (PPAR)-γ have been suggested as potential adjuvant therapy in bacterial pneumonia because of their capacity to inhibit inflammation and enhance bacterial clearance. Previous studies only assessed the effects of pretreatment with these compounds, thereby bearing less relevance for the clinical scenario. Moreover, PPAR-γ agonists have not been studied in pneumonia caused by Klebsiella pneumoniae, a common human respiratory pathogen of which antibiotic treatment is hampered by increasing antimicrobial resistance. Here we show that administration of the PPAR-γ agonist pioglitazone 6 or 8 h after infection of mice with a highly virulent strain of Klebsiella pneumoniae via the airways results in reduced cytokine and myeloperoxidase levels in the lungs at 24 h after infection, as well as reduced bacterial growth in the lungs and decreased dissemination to distant organs at 42 h post-infection. These results suggest that pioglitazone may be an interesting agent in the treatment of Klebsiella pneumonia.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.029
GPT teacher head0.284
Teacher spread0.255 · 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.

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

Citations7
Published2021
Admission routes1
Has abstractyes

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