Analysis of retrieved stroke thrombi from mechanical thrombectomy using X-ray fluorescence imaging and Fourier-transform infrared spectroscopy.
Bibliographic record
Abstract
RATIONALE: With the recent technological advances in mechanical thrombectomy, and evidence that clearly demonstrates the need for fast and effective thrombus retrieval, it remains unclear as to which device and technique combination is most effective. Defining characteristics of stroke related thrombus with advanced synchrotron based imaging techniques, may help us better understand the biochemical composition of clots.METHODS: Freshly retrieved thrombi were characterized using advanced synchrotron based imaging techniques including, X-ray fluorescence (XRF) and Fourier-transform infrared spectroscopy (FTIR) to map the distribution of biological elements (Fe, Ca) and macromolecules (proteins, amides, glutamate), respectively. Clinical data and stroke outcome is acquired for those patients included in the study.RESULTS: XRF analysis exhibited increased distribution of P and Fe in fibrin-rich white clots. FTIR analysis demonstrated an increased distribution of amide I, amide II, proteins and glutamate in white clots.CONCLUSION: Thrombus characterization, while correlating patient clinical information as well as outcomes, will improve understanding of stroke treatment outcomes.ACKNOWLEDGMENTS: I would like to thank Sharleen Maley, Lilian Urroz, Ruth Whelan, Aaron Gardner and Aaron Huber for administrative and clinical support, and all the funding agencies including, Heart & Stroke Foundation, Saskatchewan, the Saskatchewan Health Research Foundation, and the University Of Saskatchewan College Of Medicine, the College of Medicine Research and Development (CoMRAD). I would like to thank the Canadian Light Source (CLS), and Stanford Synchrotron Radiation Laboratory (SSRL) for aiding in data collection.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".