Adoption of Manganese-Doped Copper Sulfide Nanodot-Based Multimodal Magnetic Resonance Imaging in Prognosis Prediction of Patients with Ischemic Stroke Under Nutritional Care
Bibliographic record
Abstract
This study aimed to explore the application of multimodal magnetic resonance imaging (MRI) of manganese-doped copper sulfide composite nanodots (Mn/ 68 Ga-CuS@BSA/NDs) in the prediction of ischemic stroke prognosis under nutritional care. A total of 40 ischemic stroke patients with anterior circulation intracranial atherosclerotic stenosis were selected. Multimodal MRI was performed under nutritional care. Mn/ 68 Ga-CuS@BSA/ND nanocomposites were fabricated by doping divalent manganese ions (Mn 2+ ) and gallium-68 ( 68 Ga) into a CuS nanodot matrix by a nonchelating doping method. The hydrodynamic diameter of Mn/ 68 Ga-CuS@BSA/NDs was 4.56±0.78 nm, which had strong optical absorption in the range of 800~1,200 nm, and its absorption peak was located at 1,045 nm. Patients were divided into the group without ischemic events ( n = 30) and the group with ischemic events ( n = 10). Multivariate logistic regression analysis showed that diffusion-weighted imaging-alberta stroke program early computed tomography scores (DWI ASPECTs) and arterial transit time (ATA) ASPECTs were independent predictors of recurrent ischemic events. Mn/ 68 Ga-CuS@BSA/NDs had a hydrodynamic diameter of 4.56±0.78 nm and stable strong absorption in the 800–1,200nm near infrared absorption region, with good dispersion and stability. DWI ASPECT and ATA ASPECT scores for intra-arterial travel artifacts of patients with ischemic stroke can screen out high-risk patients with recurrent stroke, providing assistance for stroke treatment and prognosis.
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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 | 0.000 |
| 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 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".