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Record W1915660335 · doi:10.5539/gjhs.v8n4p260

Association of Chlamydia pneumoniae Infection With Atherosclerotic Plaque Formation

2015· article· en· W1915660335 on OpenAlexvenueno aff
Omid Assar, Azim Nejatizadeh, Farzaneh Dehghan, Mohammad Kargar, Nader Zolghadri

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsnot available
FundersHormozgan University of Medical Sciences
KeywordsChlamydiaMedicineMicrobiologyImmunologyBiology

Abstract

fetched live from OpenAlex

Atherosclerosis is a complex multifactorial disorder. Studies show that infectious microbial agents may play an important role in the development of atherosclerosis; however, these findings are conflicting. This study investigated the presence of Chlamydia pneumoniae DNA in atherosclerotic plaques of patients suffering from coronary artery disease. In a cross-sectional study, 85 patients (43 females and 42 males with mean age of 61±9.5, range 42-82 years) referred for coronary artery bypass grafting (CABG) and thoracic biopsy as the control groups were enrolled for this study. Standard questionnaires, including demographic and clinical evaluation were administered. Obtained specimens were processed and then nested polymerase chain reaction with primers for Pst1 fragment was carried out to detect Chlamydia pneumoniae DNA. Statistical analysis was done using the SPSS software. Of note, in 25 out of the 85 patients (29.4%), C. pneumoniae was detected within atherosclerotic plaques, whereas, 5 out of the 85 thoracic biopsy (5.9%) were positive for the presence of the mentioned bacteria in internal thoracic artery. There was a statistically significant association between atherosclerotic plaque (study group) and thoracic biopsy (control group) in terms of C. pneumoniae positivity (P= 0.0001). The findings of this study support the hypothesis that C. pneumoniae is associated with 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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.340
Teacher spread0.303 · 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 designObservational
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

Citations26
Published2015
Admission routes1
Has abstractyes

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