Multifunctional and stimuli-responsive liposomes in hepatocellular carcinoma diagnosis and therapy
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
Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer, mainly occurring in Asian countries with an increased incidence rate globally. Currently, several kinds of therapies have been deployed for HCC therapy including surgical resection, chemotherapy, radiotherapy and immunotherapy. However, this tumor is still incurable, requiring novel strategies for its treatment. The nanomedicine has provided the new insights regarding the treatment of cancer that liposomes as lipid-based nanoparticles, have been widely applied in cancer therapy due to their biocompaitiblity, high drug loading and ease of synthesis and modification. The current review evaluates the application of liposomes for the HCC therapy. The drugs and genes lack targeting ability into tumor tissues and cells. Therefore, loading drugs or genes on liposomes can increase their accumulation in tumor site for HCC suppression. Moreover, the stimuli-responsive liposomes including pH-, redox- and light-sensitive liposomes are able to deliver drug into tumor microenvironment to improve therapeutic index. Since a number of receptors upregulate on HCC cells, the functionalization of liposomes with lactoferrin and peptides can promote the targeting ability towards HCC cells. Moreover, phototherapy can be induced by liposomes through loading phtoosensitizers to stimulate photothermal- and photodynamic-driven ablation of HCC cells. Overall, the findings are in line with the fact that liposomes are promising nanocarriers for the treatment of HCC.
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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 | 0.000 | 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