O2–01–05: Ex vivo molecular imaging in transgenic mouse models of Alzheimer's disease
Bibliographic record
Abstract
The recent upsurge in the development of novel disease-modifying therapeutic agents for the treatment of Alzheimer's disease (AD) requires efficient and robust methods of determining pre-clinical therapeutic efficacy in transgenic mouse models. While measuring the effects of therapy on AD-type pathological changes on post-mortem tissue remains the gold-standard, conventional methods of evaluation are time-consuming, operator-biased, and limited to specific regions-of-interest. The objective is to develop and evaluate a state-of-the-art technology platform called “ex vivo molecular imaging” which allows for fully-automated, whole-brain, quantitative analysis of AD-type pathology in transgenic mice. The technology platform consists of four major components, (1) specialized preparation and ultra-high resolution digitization of whole-brain histology and immunohistochemistry (IHC) sections, (2) analysis ex-vivo data at the cellular level to generate of quantitative 2D “ex vivo molecular images”, (3) 3D reconstruction of serial 2D images for 3D quantitative maps, and (4) generation of population averages in stereotaxic space and anatomical structure-based statistical analysis of 3D data. For evaluation of this new technique, ex vivo molecular imaging volumes were produced using brains from transgenic AD mice and wild-type littermates, as well as from treated and control mice. Ex vivo molecular imaging volumes demonstrating β-amyloid burden, gliosis, and neuronal density were produced from each mouse brain. Figure 1 depicts a typical coronal view of a β-amyloid ex vivo molecular image from a 16 month-old APP transgenic mouse. Our analyses show significant regional differences between transgenic and wild-type mice, and clearly demonstrates the efficacy of the therapeutic agents evaluated. Conclusions:Ex vivo molecular imaging is a novel strategy for rapid, high-throughput analysis of the natural evolution of AD-type pathology and assessment of the therapeutic efficacy of disease-modifying agents in transgenic mice. The image processing and analysis components are fully-automated, while our specialized techniques for preparation of histology and IHC sections are as automated as possible to maximize efficiency and minimize operator-related variability. We believe that the seamless integration of ex vivo molecular imaging data with non-invasive, in vivo imaging measures of structure and function will, ultimately, allow for a comprehensive analysis of AD in animal models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".