Inflammatory Markers and Intimal Media Thickness in Diabetics with Negative Myocardial Perfusion Scan
Bibliographic record
Abstract
BACKGROUND: We compared the type and duration of diabetes mellitus (DM), patient demography, high sensitivity C-reactive protein (hsCRP), Homocysteine and other variables with IMT, to determine if these markers were correlated in diabetes (in whom technetium myocardial perfusion scan were negative) and would it be appropriate biomarkers for arthrosclerosis detection in this group of diabetics. METHODS: Forty patients with DM, without CHD history, were screened with stress sintigraphy imaging using 2 days stress/rest Technetium 99 tetrafosmin protocol, employing the standard Bruce protocol. Echocardiography study requested for each patient, two blood samples for hsCRP, were requested for each candidate three weeks apart, Lipid profiles, plasma homocysteine, and hemoglobin A1C were also requested. Finally Intima-media thickness were measured for all patients. RESULTS: There were no relationships between hsCRP level and DM duration or with the type of DM; also there were no relation between DM duration and homocysteine or between DM type and Homocysteine. Intimal media thickness was increased proportionally with the serum level of Homocysteine. CONCLUSIONS: This study did not show any role for the inflammatory markers in predicating the presence of coronary artery disease in participants with DM, without medium size artery disease, which may support that DM is not the only player in initiating atherosclerosis. KEYWORDS: Diabetes mellitus; Inflammatory markers; C-reactive protein; Myocardial ischemia; Homocysteine; Intima-media thickness.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".