LAB-EXPERIMENTAL (PRE-CLINICAL) THERAPEUTICS AND PHARMACOLOGY
Bibliographic record
Abstract
Ginsenoside Rg3 is a natural active ingredient that is extracted from Korean red ginseng root. It elevates the therapeutic effect of radiotherapy and chemotherapy, but previous studies found that the application of Rg3 is heavily limited by its low bioavailability and poor absorption via oral administration. To overcome these problems, Rg3-loaded PEG-PLGA-NPs (Rg3-NPs) were prepared by the modified spontaneous emulsification solvent diffusion (SESD) method, and the physicochemical characteristics of Rg3-NPs were investigated. We treated primary glioblastoma with 50 mM Rg3-NPs for 48h. We then used gene expression arrays (Illumina) for genome-wide expression analysis and validated the results for genes of interest by means of real-time PCR. Functional annotations were then performed using the DAVID and KEGG online tools. The results showed that the Rg3-NPs are slick and uniform, the average diameter of the nanoparticles is 75-90 nm, and their entrapment efficiency is 89.7+1.7%. MTT showed that the growth of cells can be significantly inhibited by Rg3-NPs in a dose-dependent manner. FCM testing showed Rg3-NPs can be released from the conjugate nanoparticle and react with the genes in the cell nuclei, causing changes in the gene molecules. We also found that cancer cells treated with Rg3-NPs undergo cell-cycle arrest at different checkpoints. This arrest was associated with a decrease in the mRNA levels of core regulatory genes BUB1, CDC20, TTK, and CENPE, as determined by microarray analysis and verified by real-time PCR. Furthermore, Rg3-NPs induced the expression of the apoptotic and antimigratory protein p53 in cell lines. The results of the present study, together with the results of earlier studies, show that Rg3-NPs target genes involved in theprogressionoftheM-phaseofthecellcycle.Itisassociatedwithseveralimportant pathways, which include apoptosis (p53). Rg3-NPs may be a potent cell-cycle regulation drug targeting the M-phase in glioblastoma cell lines.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.033 |
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".