MORPHIOUS: A Machine Learning Workflow to Naively Detect the Activation of Microglia and Astrocytes
Bibliographic record
Abstract
Abstract In cases of brain injury, degeneration and repair, defining microglia and astrocytic activation using cellular markers alone remains a challenging task. We developed MORPHIOUS, an unsupervised machine learning workflow that utilizes a one-class support vector machine to segment clusters of activated glia by only referencing examples of non-activated glia. Here, glial activation was triggered using focused ultrasound to permeabilize the hippocampal blood-brain barrier. Analyzing the hippocampal sections seven days later, MORPHIOUS identified two classes of microglia which showed characteristic activation features, including increases in ionized calcium-binding adapter molecule 1 expression, soma size, and de-ramification. MORPHIOUS was further used to identify clusters of activated astrocytes, which showed increased expression of glial fibrillary acidic protein and branching. Thus, by only referencing untreated glia morphologies, MORPHIOUS can identify diverse and novel manifestations of glial activation. This provides significant improvements for characterizing glial activation in cases of injury, neurodegeneration, and regeneration.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".