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Record W2126194172 · doi:10.1086/315470

Immunization with a<i>Pseudomonas aeruginosa</i>Elastase Peptide Reduces Severity of Experimental Lung Infections Due to<i>P. aeruginosa</i>or<i>Burkholderia cepacia</i>

2000· article· en· W2126194172 on OpenAlexaff
Pamela A. Sokol, Cora Kooi, R. S. Hodges, Paul J. Cachia, Donald E. Woods

Bibliographic record

VenueThe Journal of Infectious Diseases · 2000
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsPseudomonas aeruginosaKeyhole limpet hemocyaninMicrobiologyElastaseImmunizationToxoidBronchoalveolar lavageBurkholderiaImmunologyEpitopeActive immunizationLungAntibodyBiologyMedicineEnzymeBacteriaInternal medicine

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa and Burkholderia cepacia produce metalloproteases that effect lung injury. Two epitopes (peptides 15 and 42) previously identified on P. aeruginosa elastase induce the production of antibodies that neutralize protease activity. The effects of immunization with synthetic peptides based on these epitopes on experimental lung infections due to P. aeruginosa or B. cepacia were examined. Rats were immunized with peptides conjugated to keyhole limpet hemocyanin or tetanus toxoid before infection. Immunization with peptide 15 (pep15) resulted in a decrease in total cells and polymorphonuclear leukocytes in bronchoalveolar lavage (BAL) fluid and a 50%-70% decrease in lung histopathologic changes, compared with findings in controls. Immunization with peptide 42 decreased cells in BAL fluid but did not decrease lung pathologic changes. Immunization with pep15 alone was just as effective in protecting against lung injury as immunization with a combination of both peptides. These studies suggest that immunization with pep15 can reduce the severity of lung infections due to P. aeruginosa or B. cepacia.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.255
Teacher spread0.248 · 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

Citations56
Published2000
Admission routes1
Has abstractyes

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