Taking Cellular Heterogeneity Into Consideration When Modeling Astrocyte Involvement in Amyotrophic Lateral Sclerosis Using Human Induced Pluripotent Stem Cells
Bibliographic record
Abstract
Astrocytes are a large group of glial cells that perform a variety of physiological functions in the nervous system. They provide trophic, as well as structural, support to neuronal cells. Astrocytes are also involved in neuroinflammatory processes contributing to neuronal dysfunction and death. Growing evidence suggests important roles for astrocytes in non-cell autonomous mechanisms of motor neuron degeneration in amyotrophic lateral sclerosis (ALS). Understanding these mechanisms necessitates the combined use of animal and human cell-based experimental model systems, at least in part because human astrocytes display a number of unique features that cannot be recapitulated in animal models. Human induced pluripotent stem cell (hiPSC)-based approaches provide the opportunity to generate disease-relevant human astrocytes to investigate the roles of these cells in ALS. These approaches are facing the growing recognition that there are heterogenous populations of astrocytes in the nervous system which are not functionally equivalent. This review will discuss the importance of taking astrocyte heterogeneity into consideration when designing hiPSC-based strategies aimed at generating the most informative preparations to study the contribution of astrocytes to ALS pathophysiology.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".