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Developing a Novel Therapy for Bacterial Pneumonia

2018· article· en· W3174650438 on OpenAlexaff
Brandon Baer, Christina Arsenault, Lynda McCaig, Cory Yamashita, Ruud A. W. Veldhuizen

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsWestern University
Fundersnot available
KeywordsIn vivoPneumoniaMedicineMicrobiologyInflammationPseudomonas aeruginosaAntibioticsLungCath labBacteriaImmunologyBiologyInternal medicineBiotechnology

Abstract

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Background Bacterial pneumonia is a leading cause of death worldwide. Unfortunately, new treatments are faced with several major hurdles. Firstly, the incidence of antibiotic resistance is increasing. Secondly, both acute and chronic lung infections are often accompanied by maladaptive inflammatory responses linked to poor outcomes. Finally, the structure of the lung makes delivery of therapeutics to the sites of infection challenging. As a potential treatment for bacterial pneumonia, the current study combines a host‐defense peptide (CATH‐2), previously shown to kill antibiotic‐resistant bacteria and reduce inflammation, with an exogenous surfactant (BLES), capable of enhancing spreading throughout the lung. Objectives 1) Quantify the transport CATH‐2 by BLES in vitro , 2) Assess the antimicrobial and anti‐inflammatory properties of BLES+CATH‐2 subsequent to spreading across a surface and 3) Investigate the immunomodulatory effects of BLES+CATH‐2 in vivo . Hypothesis The mixture of BLES+CATH‐2 will improve transport of CATH‐2 allowing for effective bacterial killing and reductions in inflammation at distal sites in vitro and in vivo. Methods Fluorescently‐labelled CATH‐2 was used to track its movement as it spread across a Wet Bridge Transfer system alone or in combination with BLES. Bacterial killing and anti‐inflammatory properties were assessed by seeding either a lab strain of Pseudomonas aeruginosa or RAW 264.7 macrophages to the distal well of the wet bridge system. The macrophages were stimulated with heat‐killed P. aeruginosa 15 minutes prior to the administration of saline, BLES, CATH‐2 or BLES+CATH‐2 in the proximal well. The fluid in each well was analyzed for cytokine content and bacterial killing. Additionally, a non‐infectious model of bacterial pneumonia was used, where mice were instilled with heat‐killed P. aeruginosa or saline. This first instillation was then followed by either saline, BLES, CATH‐2 or BLES+CATH‐2. All mice were monitored for 4 hours before being euthanized. Bronchoalveolar lavage fluid was collected and analyzed for cell counts, cell differentials, and cytokine concentrations. Results Fluorescence spectrometry revealed that significantly more CATH‐2 was transferred across the bridge when combined with BLES compared to CATH‐2 by itself. Additionally, only the combination of BLES+CATH‐2 showed significant improvements in bacterial killing and reducing inflammation across the wet bridge. Mice administered heat‐killed bacteria showed significant increases in the number of inflammatory cells, neutrophils and lavage IL‐6, TNF‐α and KC content compared to saline control. Instillation of BLES+CATH‐2 after an instillation of heat‐killed bacteria showed significant reductions across all markers of inflammation compared to saline, BLES or CATH‐2 alone. Discussion These results support BLES as an effective vehicle for the transport of CATH‐2 and that the mixture has potent antimicrobial and anti‐inflammatory properties. Our novel approach allowed us to rapidly assess the efficacy and spreading capabilities of BLES+CATH‐2. Additionally, the results support BLES+CATH‐2 as a therapy which can overcome the delivery problem hindering pulmonary therapies and reach distal sites of inflammation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.034
GPT teacher head0.264
Teacher spread0.229 · 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 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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