Neurotensin polyplex as an efficient carrier for delivering the human GDNF gene into nigral dopamine neurons of hemiparkinsonian rats
Bibliographic record
Abstract
Recently we showed that the neurotensin polyplex is a nanoparticle carrier system that targets reporter genes in nigral dopamine neurons in vivo. Herein, we report its first practical application in experimental parkinsonism, which consisted of transfecting dopamine neurons with the gene coding for human glial cell line-derived neurotrophic factor (hGDNF). Hemiparkinsonism was induced in rats by a single dose of 6-hydroxydopamine (30 microg) into the ventrolateral part of the striatum. We showed that transfection of the hGDNF gene into the substantia nigra of rats 1 week after the neurotoxin injection produced biochemical, anatomical, and functional recovery from hemiparkinsonism. RT-PCR analysis showed mRNA expression of exogenous hGDNF in the transfected substantia nigra. Western blot analysis verified transgene expression by recognizing the flag epitope added at the C-terminus of the hGDNF polypeptide, which was found mainly in dopamine neurons by double immunofluorescence techniques. These data indicate that the neurotensin polyplex holds great promise for the neuroprotective therapy of Parkinson disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".