ZIF-C as non-viral delivery system for CRISPR/Cas9 mediated hTERT knockdown in cancer cells
Bibliographic record
Abstract
Telomerase, a ribonucleoprotein coded by the hTERT gene, plays an important role in cellular immortalization and carcinogenesis. hTERT is a suitable target for cancer therapeutics as its activity is highly upregulated in 85-90% of cancer cells but absent in normal somatic cells. Here, we target the hTERT gene at the DNA level by applying the Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas9 technology encapsulated in a recently discovered Metal-Organic Framework (MOF). We show that the MOF subtype ‘ZIF-C’ can efficiently load the hTERT targeting CRISPR system (CrhTERT@ZIF-C) and protect it from enzymatic degradation. The CrhTERT@ZIF-C is endocytosed by cancer cells and successfully disrupts the hTERT gene. The resultant inhibition of hTERT decreases cellular proliferation and causes apoptotic cancer cell death. Furthermore, hTERT knockdown shows a significant reduction in tumor metastasis and alters protein expression. Thus, our results conclusively establish ZIF-C based targeting of hTERT as a highly promising and novel approach for gene therapy in cancer.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".