In-vivo and real-time ultrasonic monitoring of red blood cell aggregation with the structure factor size and attenuation estimator during and after cardiopulmonary bypass surgery in swine
Bibliographic record
Abstract
Cardiopulmonary bypass (CPB) used during cardiac surgery induces a systemic inflammatory response. The severity of that response has been shown to be proportional to patient outcome. Markers of inflammation are typically obtained intermittently through blood testing with variable delay. Red blood cell (RBC) aggregation is a surrogate marker of inflammation that can be quantified with ultrasound. It could therefore represent a real-time inflammation monitoring instrument for patient care. However, the relationship between markers of inflammation during CPB and RBC aggregation is unknown. Seven swine underwent a 90 min procedure with CPB followed by a 120 min reperfusion. To induce a more severe inflammatory reaction, lipopolysaccharide (LPS) was administrated 24 h prior to surgery and just before the CPB in 4 pigs (LPS group). Other pigs composed the control group (CONT group, n=3). A RBC aggregation parameter was extracted from ultrasonic images acquired over the femoral vein. The mean fractal size of aggregates (D), which was determined with the Structure Factor Size and Attenuation Estimator (SFSAE), was calculated. Measurements were performed at the beginning (TCPB15), after 30 min. (TCPB30) and at the end (TCPB90) of CPB. Measurements were repeated during reperfusion after 30 and 120 min of recirculation (TREP30and TREP120). The temporal evolution of interleukin 6 (IL-6), a blood inflammatory marker, was also assessed. D exhibited a significant increase from TCPB30to TREP120in all swine, matching IL-6 evolution. The LPS group presented significantly higher RBC aggregation during the reperfusion compared with control pigs, which indicates potentially high sensitivity of the SFSAE. This new cellular imaging modality may become a real-time non-invasive monitoring technique to anticipate inflammation-related complications during high-risk surgery or sepsis situation.
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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 | 0.001 | 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 | 0.001 |
| 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 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".