Effect of Lentiviral shRNA of Nogo Receptor on Rat Cortex Neuron Axon Outgrowth
Bibliographic record
Abstract
BACKGROUND AND AIMS: Axon growth is crucial for injured neural tissue to recover; however it is difficult to achieve in general. Axon outgrowth is inhibited by the activation of the Nogo receptor (NgR) by one of three different ligands. The present study aimed to suppress the inhibitory effect of the three inhibitory proteins to facilitate axon outgrowth. METHODS: A lentiviral vector, siNgR199 (that has the capacity to interfere with the gene of NgR expression), was constructed for suppressing the gene transcription of NgR. Rat cortex neurons and oligodendrocytes were prepared to observe the effect of siNgR199 on facilitating axon outgrowth. RESULTS: After transfection, the lentiviral siRNA of NgR remained in target neurons for almost two weeks whereas the conventional siRNA of NgR remained in neurons less than five days. Lentivirus-mediated delivery of exogenous small interfering RNA (siNgR199) targeting NgR significantly reduced the expression of this receptor and promoted axon outgrowth. In contrast, provision of naked siRNA targeting NgR (NgRsiRNA) showed less inhibitory effect on NgR protein expression and did not affect axon outgrowth. CONCLUSIONS: Lentiviral siRNA of NgR effectively suppresses the expression of NgR in cultured neurons that facilitates the axon outgrowth. The data implicate that lentiviral siRNA of NgR has therapeutic potential in facilitating the recovery of injured neural tissue.
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) | 0.001 | 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 | 0.001 |
| 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".