Adjunctive laser‐stimulated stem‐cells therapy to primary reperfusion in acute myocardial infarction in humans: Safety and feasibility study
Bibliographic record
Abstract
BACKGROUND: Low-level laser therapy (LLLT) has photobiostimulatory effects on stem cells and may offer cardioprotection. This cell-based therapy may compliment primary percutaneous coronary intervention (PPCI) in patients with ST-segment elevation myocardial infarction (STEMI). OBJECTIVE: In this randomized control trial, our primary objective was to determine the safety and feasibility of LLLT application to the bone marrow in patients with STEMI undergoing PPCI. METHODS: We randomly assigned patients undergoing PPCI to LLLT or non-laser therapy (NLT). In the LLLT group, 100 s of laser therapy was applied to the tibia bone prior to PPCI, as well as 24 and 72 h post-PPCI. In the control group, the power source was turned off. The primary outcome was the difference in door-to-balloon (D2B) time, and additional outcomes included differences in circulating cell counts, cardiac enzymes, and left-ventricular ejection fraction (LVEF) at pre-specified intervals post-PPCI. RESULTS: Twenty-four patients were randomized to LLLT (N = 12) or NLT (N = 12). No adverse effects of the treatment were detected. The D2B time was not significantly different between the groups (41 ± 8 vs 48 ± 1 min; P = 0.73). Creatinine Phosphokinase area under the curve, was lower after LLLT (22 ± 10) compared to NLT (49 ± 12), but this was not statistically significant (P = 0.08). Troponin-T was significantly lower after LLLT (2.7 ± 1.4 ng/mL) in comparison to NLT (5.2 ± 1.8 ng/mL. P < 0.05). At 9 months, LVEF improved in both groups without a significant difference between LLLT (55 ± 9%) and NLT (52 ± 9%; P = 0.90). CONCLUSION: LLLT is a safe and feasible adjunctive cell-based therapy to PPCI that may benefit ischemic myocardium.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".