Bibliographic record
Abstract
Because of their nano-size, biological compatibility, and ability to precisely engineer antigens displayed, payloads packaged, and destinations targeted, nanobiomaterials are gaining traction as next-generation therapeutic tools. Oncolytic viruses were the first to be exploited in cancer immunotherapy because these are natural cell killers and, in some cases, highly selective for cancerous cells. Further, oncolytic viruses can be engineered to encode immune-stimulators and therapeutic genes. However, for oncolytic viruses to work, it is essential to develop these as viable viruses with the ability to infect. This raises safety concerns and poses hurdles in regulatory approval. To circumvent this limitation, non-replicating viruses and virus-like particles have been explored for immunotherapeutic applications. The advantage of these is their inability to infect mammals, thereby eliminating bio-safety concerns. Nonetheless, concerns related to toxicity need to be addressed in each case. Several virus-like particle candidates are currently in preclinical development stages and show promise for clinical use via intertumoral administration, also referred to as vaccination in situ. In cases where in situ administration is not possible due to the absence of solid tumours or inaccessibility of the tumour, nano-biomaterials for systemic administration are desired, and extracellular vesicles fit this bill. Exosomes, in particular, can provide controlled abscopal effects – a property desirable for the treatment of metastatic cancer. This chapter discusses the state-of-the-art in the development of nano-biomaterials for immunotherapy. With a plethora of candidates in development and over two hundred clinical trials ongoing worldwide, nanobiomaterials hold great promise as effective cancer immunotherapies with minimal side effects.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".