Guggulsterone-releasing microspheres direct the differentiation of human induced pluripotent stem cells into neural phenotypes
Bibliographic record
Abstract
Parkinson's disease (PD), a common neurodegenerative disorder, results from the loss of motor function when dopaminergic neurons (DNs) in the brain selectively degenerate. While pluripotent stem cells (PSCs) show promise for generating replacement neurons, current protocols for generating terminally differentiated DNs require a complicated cocktail of factors. Recent work demonstrated that a naturally occurring steroid called guggulsterone effectively differentiated PSCs into DNs, simplifying this process. In this study, we encapsulated guggulsterone into novel poly-ε-caprolactone-based microspheres and characterized its release profile over 44 d in vitro, demonstrating we can control its release over time. These guggulsterone-releasing microspheres were also successfully incorporated in human induced pluripotent stem cell-derived cellular aggregates under feeder-free and xeno-free conditions and cultured for 20 d to determine their effect on differentiation. All cultures stained positive for the early neuronal marker TUJ1 and guggulsterone microsphere-incorporated aggregates did not adversely affect neurite length and branching. Guggulsterone microsphere incorporated aggregates exhibited the highest levels of TUJ1 expression as well as high Olig 2 expression, an inhibitor of the STAT3 astrogenesis pathway previously known as a target for guggulsterone in cancer treatment. Together, this study represents an important first step towards engineered neural tissues consisting of guggulsterone microspheres and PSCs for generating DNs that could eventually be evaluated in a pre-clinical model of PD.
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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) | 0.000 | 0.000 |
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".