Fibrosis independent atrial fibrillation in older patients is driven by substrate leukocyte infiltration: diagnostic and prognostic implications to patients undergoing cardiac surgery
Bibliographic record
Abstract
BACKGROUND: The objectives of the study were to characterize and quantify cellular inflammation and structural remodeling of human atria and correlate findings with molecular markers of inflammation and patient surrogate outcome. METHODS: Voluntary participants undergoing heart surgery were enrolled in the study and blood samples were collected prior to surgery, and right atrium samples were harvested intraoperatively. Blood samples were analyzed by flow cytometry and complete blood counts. Atrial samples were divided for fixed fibrosis analysis, homogenized for cytokine analysis and digested for single cell suspension flow cytometry. RESULTS: A total of 18 patients were enrolled and samples assessed. Isolated cells from the atria revealed a CD45+ population of ~ 20%, confirming a large number of leukocytes. Further characterization revealed this population as 57% lymphocytes and 26% monocyte/macrophages (MoΦ), with the majority of the latter cells being classical (CD14++/CD16-). Interstitial fibrosis was present in 87% of samples and correlated significantly with patient age. Older patients (> 65) had significantly more atrial fibrosis and cellular inflammation. AFib patients had no distinguishing feature of atrial fibrosis and had significantly greater CD45+ MoΦ, increased expression of MMP9 and presented with a significant correlation in length of stay to CCL-2/MCP-1 and NLR (neutrophil-to-lymphocyte ratio). CONCLUSION: Atrial fibrosis is correlated with age and not determinate to AFib. However, severity of atrial leukocyte infiltration and markers of matrix degradation are determinant to AFib. This also correlated with CCL2 (or MCP-1) and NLR-indicative of marked inflammation. These data show the potential importance of diagnostic and prognostic assessments that could inform clinical decision making in regard to the intensity of AFib patient management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".