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Possibilities for therapeutic interventions in disrupting <i>Chlamydophila pneumoniae</i> involvement in atherosclerosis

2010· review· en· W1492781710 on OpenAlexaff
Justin Deniset, Grant N. Pierce

Bibliographic record

VenueFundamental and Clinical Pharmacology · 2010
Typereview
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsChlamydophila pneumoniaeAntibioticsChlamydiaPneumoniaObligateBiologyImmunologyIntracellular parasitePathogenMicrobiologyMedicineChlamydiaceaeImmune systemInternal medicine

Abstract

fetched live from OpenAlex

Strong sero-epidemiologic, pathologic, and experimental evidence suggests that Chlamydophila pneumoniae (Cpn) infection may play a causative role in the development of atherosclerosis. Cpn is an obligate intracellular gram-negative bacterium that is responsible for 10% of cases of community-acquired pneumonia. In addition to its presence in the respiratory tract, live Cpn has been found within atherosclerotic plaques. Experimental findings have established Cpn's ability to infect vascular cells and elicit important atherogenic responses. Furthermore, Cpn infection can promote atherosclerotic development in different animal models. To date however, large-scale antibiotic clinical trials have not been effective in preventing major cardiovascular events. It is becoming apparent that Cpn undergoes a persistent state of infection, which is refractory to current chlamydial antibiotics. New treatment strategies that are effective toward acute and persistent forms of Cpn infection are needed in order to effectively eradicate the bacterium within the vascular wall. Possible therapeutics targets include Cpn-specific proteins and machinery directly involved in their survival, replication and maintenance. Alternatively, selectively targeting host cell pathways and machinery required for Cpn's actions in vascular cells also represent potential treatment strategies for atherosclerosis.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.226
GPT teacher head0.506
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2010
Admission routes1
Has abstractyes

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