Targeted immunotherapy and nanomedicine for rhabdomyosarcoma: The way of the future
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
Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood. Histology separates two main subtypes: embryonal RMS (eRMS; 60%-70%) and alveolar RMS (aRMS; 20%-30%). The aggressive aRMS carry one of two characteristic chromosomal translocations that result in the expression of a PAX3::FOXO1 or PAX7::FOXO1 fusion transcription factor; therefore, aRMS are now classified as fusion-positive (FP) RMS. Embryonal RMS have a better prognosis and are clinically indistinguishable from fusion-negative (FN) RMS. Next to histology and molecular characteristics, RMS risk groupings are now available defining low risk tumors with excellent outcomes and advanced stage disease with poor prognosis, with an overall survival of about only 20% despite intensified multimodal treatment. Therefore, development of novel effective targeted strategies to increase survival and to decrease long-term side effects is urgently needed. Recently, immunotherapies and nanomedicine have been emerging for potent and effective tumor treatments with minimal side effects, raising hopes for effective and safe cures for RMS patients. This review aims to describe the most relevant preclinical and clinical studies in immunotherapy and targeted nanomedicine performed so far in RMS and to provide an insight in future developments.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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