In-situ characterization of red blood cell aggregation measured with high frequency ultrasound in type 2 diabetic patients
Bibliographic record
Abstract
The present study investigates in-vivo and in-situ red blood cell (RBC) aggregation in both upper and lower limbs with high frequency ultrasound in diabetic patients presenting lower limb ischemia. Ultrasonic radio-frequency images were acquired at 25 MHz from both cephalic (CEP) and great saphenous (GSV) veins in 10 healthy subjects (CONT group), 6 diabetic patients presenting intermittent claudication (Grade I group, i.e. Rutherford classification), and 4 diabetic patients affected by chronic critical ischemia (Grade II/III group). In addition to hemodynamic parameters (i.e. vessel diameters, blood flow velocities and shear rates), the mean fractal size of RBC aggregates (D) was estimated with the Structure Factor Size and Attenuation Estimator. In the Grade I group, D tended to be higher than CONT for the GSV only. Moreover, D values were significantly higher in GSV versus CEP in the Grade I group. Surprisingly, we also observed significantly lower D values in Grade II/III patients compared to Grade I for the GSV. This could be due to arteriovenous communications (and the concomitant increase in blood velocity) occurring in patients with chronic critical ischemia. In summary, we have been able to observe local differences in RBC aggregation in the lower limb with ultrasound. Rheological disorders are likely involved in the progression of lower limb ischemic phenomena. The proposed quantitative cellular imaging method could become an additional tool for the management of diabetic patients presenting lower limb ischemia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".