Nanotherapeutic Modulation of Human Neural Cells and Glioblastoma in Organoids and Monocultures
Bibliographic record
Abstract
Inflammatory processes in the brain are orchestrated by microglia and astrocytes in response to activators such as pathogen-associated molecular patterns, danger-associated molecular patterns and some nanostructures. Microglia are the primary immune responders in the brain and initiate responses amplified by astrocytes through intercellular signaling. Intercellular communication between neural cells can be studied in cerebral organoids, co-cultures or in vivo. We used human cerebral organoids and glioblastoma co-cultures to study glia modulation by dendritic polyglycerol sulfate (dPGS). dPGS is an extensively studied nanostructure with inherent anti-inflammatory properties. Under inflammatory conditions, lipocalin-2 levels in astrocytes are markedly increased and indirectly enhanced by soluble factors released from hyperactive microglia. dPGS is an effective anti-inflammatory modulator of these markers. Our results show that dPGS can enter neural cells in cerebral organoids and glial cells in monocultures in a time-dependent manner. dPGS markedly reduces lipocalin-2 abundance in the neural cells. Glioblastoma tumoroids of astrocytic origin respond to activated microglia with enhanced invasiveness, whereas conditioned media from dPGS-treated microglia reduce tumoroid invasiveness. Considering that many nanostructures have only been tested in cancer cells and rodent models, experiments in human 3D cerebral organoids and co-cultures are complementary in vitro models to evaluate nanotherapeutics in the pre-clinical setting. Thoroughly characterized organoids and standardized procedures for their preparation are prerequisites to gain information of translational value in nanomedicine. This study provides data for a well-characterized dendrimer (dPGS) that modulates the activation state of human microglia implicated in brain tumor invasiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".