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Record W2039533000 · doi:10.1097/jom.0b013e31803220ef

Influence of Personal Exposure to Particulate Air Pollution on Cardiovascular Physiology and Biomarkers of Inflammation and Oxidative Stress in Subjects With Diabetes

2007· article· en· W2039533000 on OpenAlexafffund
Ling Liu, Terrence D. Ruddy, Mary Dalipaj, Mieczysław Szyszkowicz, Hongyu You, Raymond Poon, Amanda J. Wheeler, Robert Dales

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsHealth Canada
FundersUniversity of Ottawa
KeywordsTBARSOxidative stressMedicineBrachial arteryDiabetes mellitusInternal medicineInflammationBlood pressureSystemic inflammationEndocrinologyCardiologyPhysiologyLipid peroxidation

Abstract

fetched live from OpenAlex

OBJECTIVE: We investigated whether personal exposure to particulate matter </= 10 microm in diameter (PM10) contributes to impaired cardiovascular function and increased systemic inflammation and oxidative stress in diabetic patients. METHODS: We monitored 25 patients' personal exposure to PM10 for 24 hours and then measured their heart rate, blood pressure, brachial arterial diameter, flow-mediated vasodilation (FMD), plasma cytokines, and thiobarbituric acid reactive substances (TBARS), which is an oxidative stress marker. We repeated this procedure for 7 weeks on each subject. We tested the associations using mixed-effects models. RESULTS: PM10 was significantly positively associated with FMD and TBARS but inversely associated with end-systolic basal brachial arterial diameter (P < 0.05). Moreover, in subjects not taking vasoactive medications, PM10 was significantly positively associated with blood pressure but inversely associated with artery flow. CONCLUSION: Elevated PM10 may contribute to oxidative stress and impaired cardiovascular function in patients with diabetes mellitus.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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

Citations85
Published2007
Admission routes2
Has abstractyes

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