P3‐222: <i>In vitro</i> and <i>in vivo</i> study of a magnetic resonance imaging (MRI)/fluorescent contrast agent for detection in Alzheimer's disease
Bibliographic record
Abstract
A new magnetic resonance imaging (MRI)/fluorescent contrast agent, DOTA-CAT, has been designed to detect a biological target. DOTA-CAT consists of a DOTA-caged metal ion, coupled to a cell-penetrating-peptide sequence and a Cathepsin-D (CatD) recognition site. CatD is a lysosomal protease found in elevated levels in amyloid plaques and neuronal cells of Alzheimer's disease (AD) patients. A fluorescent probe, Oregon Green, was also attached to DOTA-CAT to monitor its uptake optically. The purpose of this study was to demonstrate detection of DOTA-CAT in the MRI environment using the on-resonance paramagnetic chemical exchange effect (OPARACHEE) and optically in AD mouse models. The OPARACHEE contrast of the agent was studied in 5% cross-linked bovine serum albumin (BSA), which simulates tissue. OPARACHEE contrast was defined as the difference between the bulk water signal intensity change with and without agent present. Images were acquired on a 9.4 Tesla MRI using a fast spin echo pulse sequence (Field of view = 25.6x25.6 mm2, data matrix: 256x256, repetition time = 4 s, echo time = 10 ms, echo train length = 4 and 2 averages), preceded by a 0.24 second 6.0 microtesla WALTZ-16 pulse train at 23C. A reference image was also acquired without the WALTZ-16 preparation pulse. Cranial openings were produced in 12-month old Alzheimer's Precursor Protein (APP) mice (n = 5) to expose the brain cortex. To verify agent uptake in-vivo, tail vein injections of 1 mM DOTA-CAT were performed to monitor agent uptake and washout in the brain cortex using in-vivo confocal microscopy. The OPARACHEE contrast generated as a function of DOTA-CAT concentration in 5% BSA is shown in Figure 1. A 1% OPARACHEE contrast required ˜1.5 mM DOTA-CAT. In optical experiments, DOTA-CAT was observed on the surface of the brain within 5 seconds of injection and signal remained for at least 30 minutes (Figure 1).
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 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 | 0.001 | 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 | 0.000 |
| 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".