Lentiviral vector-based therapy in head and neck cancer (Review)
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is the sixth most common neoplasm worldwide. Despite advances in multimodality treatments involving surgery, radiation and chemotherapy, the five-year survival rate has remained at ~50% for the past 35 years. Therefore, the early detection of recurrent or persistent disease is extremely important. Replication-incompetent HIV-1-based lentiviral vectors have emerged as powerful and safe tools for gene delivery. Commonly, HNSCC is a locoregional disease that presents at or close to the body surface. Thus, HNSCC is amendable to intratumoral injections of gene therapy vectors aimed at correcting defects associated with tumor suppressor genes to induce the direct cytotoxicity of cancer cells or immune modulation to promote antitumor immunity. Current investigations analyzing HNSCC gene mutations and stem cell markers and the cancer immunoediting concept are creating exciting therapeutic opportunities for lentiviral and other gene transfer vectors. The present review reports specific examples of the current applications of lentiviral vectors in HNSCC.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".