Characterization of human iPSC-derived astrocytes with potential for disease modeling and drug discovery
Bibliographic record
Abstract
ABSTRACT Astrocytes play a number of key functions in health and disease. Reactivation of astrocytes occurs in most, if not all, neurological diseases. Most current information on the mechanisms underlying astrogliosis derives from studies using rodent experimental systems, mainly because the ability to study human astrocytes under healthy and pathological conditions has been hampered by the difficulty in obtaining primary human astrocytes. Here we describe robust and reliable derivation of astrocytes from human induced pluripotent stem cells (iPSCs). Phenotypically characterized human iPSC-derived astrocytes exhibit typical traits of physiological astrocytes, including spontaneous and induced calcium transients. Moreover, human iPSC-derived astrocytes respond to stimulation with a pro-inflammatory combination of tumor necrosis factor alpha, interleukin 1-alpha, and complement component C1q by undergoing changes in gene expression patterns suggesting acquisition of a reactive astrocyte phenotype. Together, these findings provide evidence suggesting that human iPSC-derived astrocytes are a suitable experimental model system to study astrocyte function and reactivation in healthy and pathological conditions of the human nervous system.
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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 | 0.000 | 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) | 0.001 | 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".