EFFECT OF AUTOLOGOUS BONE MARROW MONONUCLEAR CELLS TRANSPLANTATION IN DIABETIC PATIENTS WITH ST-SEGMENT ELEVATION MYOCARDIAL INFARCTION
Bibliographic record
Abstract
Objectives To investigate the efficacy and proposed mechanism of bone marrow mononuclear cells (BMMNCs) transplantation for diabetic and non-diabetic patients with ST-segment elevation myocardial infarction (STEMI). Methods One hundred and sixteen patients with STEMI who had successfully undergone percutaneous coronary intervention (PCI) were divided into a diabetic group (n=51) and non-diabetic group (n=65). All of the patients received intracoronary injection of BMMNCs. Results Diabetes down-regulated IGF-1, IGFBP-5, VEGF, SDF-1, IL-6, IL-1α and TNF-α expression and affected the expression of Bmi-1, Gfi1, Tel and Hox-B4 which could prevent premature senescence and maintain the self-renewal capacity of stem cells. Event-free survival rates were not statistically different between the diabetic and non-diabetic group (80% vs 72.5%, p=0.382). LV ejection fraction (LVEF) and wall motion score index (WMSI) were evaluated by echocardiography and found to be significantly improved in the non-diabetic group compared to the diabetic group over the 4-year follow-up period. Improved myocardial perfusion and reduced infarct size in the non-diabetic group compared to the diabetic group was verified using single-photon emission computed tomographic (SPECT) imaging. The non-diabetic group also had reduced anginal symptoms as assessed by changes in their Seattle Angina Questionnaire scores and Canadian Cardiovascular Society (CCS) Functional Angina classification. An improvement of 6-min walk distance (6MWD) was also noted to be higher in the non-diabetic group during the follow-up period. Conclusions This study indicates that the beneficial effect of BMMNCs transplantation for STEMI is less pronounced in diabetic patients. The mechanism is associated with decreased BMMNCs function in diabetic patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".