A GoP based FEC technique for packet based video streaming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, cell membrane camouflaged nanoparticles (NPs) endowed with natural cellular functions have been extensively studied in various biomedical fields. However, there are few reports about such biomimetic NPs used to codeliver chemodrug and genes for synergistic cancer treatment up to now. Herein, we first prepare chemodrug-gene nanoparticles (Mito-Her2 NPs) by the electrostatic interaction coself-assembly of mitoxantrone hydrochloride (Mito) and human epidermal growth factor receptor-2 antisense oligonucleotide (Her2 ASO). Then, Mito-Her2 NPs are coated by a hybrid membrane (RSHM), consisting of the red blood cell membrane (RBCM) and the SKOV3 ovarian cancer cell membrane (SCM), to produce biomimetic chemodrug-gene nanoparticles (Mito-Her2@RSHM NPs) for combination therapy of ovarian cancer. Mito-Her2@RSHM NPs integrate the advantages of RBCM (e.g., good immune evasion capability and long circulation lifetime in the blood) and SCM (e.g., highly specific cognate recognition) together and improve the anticancer efficacy of Mito-Her2 NPs. The results show that Mito-Her2@RSHM NPs can be devoured by SKOV3 ovarian cancer cells and effectively degraded to release Her2 ASOs and Mito simultaneously. Her2 ASOs can inhibit the expression of endogenous Her2 genes and recover cancer cells' sensitivity to Mito, which ultimately led to a high apoptosis rate of 75.7% in vitro. Mito-Her2@RSHM NPs also show a high tumor suppression rate of 83.33 ± 4.16% in vivo without significant damage to normal tissues. In summary, Mito-Her2@RSHM NPs would be expected as a versatile and safe nanodrug delivery platform with high efficiency for chemo-gene combined cancer treatment.
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.
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 | 0.003 | 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 it