Proteomic Characterization of Preclinical Neurodegenerative Models with the NULISA™ Multiplex Murine Neuroinflammation Panel 2206
Bibliographic record
Abstract
Abstract Description Preclinical models of Alzheimer’s Disease (AD) have been valuable in deciphering the dynamics of disease pathogenesis and evaluating therapeutic strategies. However, the degree to which these models reflect the biology of the disease in humans is not known. With a growing number of preclinical models, deeper proteomic characterization can help to evaluate the differences between these model systems and their clinical utility. To this end, we have developed a 120-plex Murine Neuroinflammation Panel for detection of key neurodegenerative targets, inflammatory cytokines and growth factors. The panel was optimized and validated for murine plasma, CSF and brain lysates. Dilutional linearity, assay reproducibility and detectability were assessed at different sample input volumes. Overall, our studies show robust assay performance with <10% median intra-assay CV and <15% median inter-assay CV. Additionally, detectability in plasma is > 95%, reflecting the high sensitivity of the NULISA™ technology with background suppression mechanisms to achieve detection of a wide dynamic range of proteins. Furthermore, dilutional linearity studies showed good linearity with signal-to-noise ratio >2 for majority of the targets in the panel. Application of this panel in murine models of AD, traumatic brain injury and common strains used for preclinical research show the broad utility of the multiplex NULISAseq Murine Panel for analyzing mechanisms of neuroinflammation and disease pathology in mice. Topic Categories Neuroimmunology (NEUR)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".