T1WI‐SWI Dual Modal Magnetic Resonance Nanoprobes for Accurate Diagnosis of Early Stage Alzheimer's Disease
Bibliographic record
Abstract
Abstract Beta‐site APP‐cleaving enzyme 1 (BACE1), a critical rate‐limiting enzyme that synthesizes β‐amyloid peptide (Aβ), is an important marker of early pathological changes in Alzheimer's disease (AD). Early small plaques cannot be accurately detected using traditional Magnetic resonance imaging (MRI) probes. Therefore, magnetic resonance tuning (MRET) and susceptibility weighted imaging (SWI)‐based smart responsive MR nanoprobes are designed to achieve the sensitive detection of BACE1 and Aβ plaques. This probe is modified with a blood‐brain barrier‐penetrating targeting peptide that enables its reach to the AD microenvironment. The enhancement of T1WI signals owing to the MRET effect caused by the separation of probes in response to BACE1 is used to reflect real‐time BACE1 changes. When Aβ plaques are present, the remaining probes that bound around Aβ plaques underwent in situ thiol cross‐linking under the action of peroxynitrite (ONOO − ) in the AD microenvironment, and SWI can magnify magnetic susceptibility differences, which significantly increased the Aβ plaque detection sensitivity. This probe combines the advantages of T1WI and SWI and can simultaneously visualize and reflect the BACE1 level and Aβ plaque distribution. This approach overcomes the limitations of traditional single‐target probes and provides a new noninvasive and highly accurate strategy for the early diagnosis of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".