Dendritic Polyglycerol Sulfates in the Prevention of Synaptic Loss and Mechanism of Action on Glia
Bibliographic record
Abstract
Dendritic polyglycerols (dPG), particularly dendritic polyglycerol sulfates (dPGS), have been intensively studied due to their intrinsic anti-inflammatory activity. As related to brain pathologies involving neuroinflammation, the current study examined if dPG and dPGS can (i) regulate neuroglial activation, and (ii) normalize the morphology and function of excitatory postsynaptic dendritic spines adversely affected by the neurotoxic 42 amino acid amyloid-β (Aβ 42 ) peptide of Alzheimer disease (AD). The exact role of neuroglia, such as microglia and astrocytes, remains controversial especially their positive and negative impact on inflammatory processes in AD. To test dPGS effectiveness in AD models we used primary neuroglia and organotypic hippocampal slice cultures exposed to Aβ 42 peptide. Overall, our data indicate that dPGS is taken up by both microglia and astrocytes in a concentration- and time-dependent manner. The mechanism of action of dPGS involves binding to Aβ 42, i.e., a direct interaction between dPGS and Aβ 42 species interfered with Aβ fibril formation and reduced the production of the neuroinflammagen lipocalin-2 (LCN2) mainly in astrocytes. Moreover, dPGS normalized the impairment of neuroglia and prevented the loss of dendritic spines at excitatory synapses in the hippocampus. In summary, dPGS has desirable therapeutic properties that may help reduce amyloid-induced neuroinflammation and neurotoxicity in AD.
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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 | 0.000 |
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".