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Record W4388288448 · doi:10.3390/cancers15215269

Rhabdomyosarcoma: Current Therapy, Challenges, and Future Approaches to Treatment Strategies

2023· review· en· W4388288448 on OpenAlexafffund
Ali Zarrabi, David Perrin, Mahboubeh Kavoosi, Micah Sommer, Serap Sezen, Parvaneh Mehrbod, Bhavya Bhushan, Filip Machaj, Jakub Rosik, Philip Kawalec, Saba Afifi, Seyed Mohammadreza Bolandi, Peiman Koleini, Mohsen Taheri, Tayyebeh Madrakian, Marek Łoś, Benjamin B. Lindsey, Nilüfer Çakır, Atefeh Zarepour, Kiavash Hushmandi, Ali Fallah, Bahattin Koç, Arezoo Khosravi, Mazaher Ahmadi, Susan E. Logue, Gorka Orive, Stevan Pecic, Joseph W. Gordon, Saeid Ghavami

Bibliographic record

VenueCancers · 2023
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsChildren's Hospital Research Institute of ManitobaCancerCare ManitobaHealth Sciences CentreResearch Institute in Oncology and HematologyManitoba HealthMcGill UniversityUniversity of Manitoba
FundersChildren's Hospital Research Institute of Manitoba
KeywordsRhabdomyosarcomaMedicineZebrafishRadiation therapyAlveolar rhabdomyosarcomaDiseaseSarcomaChemotherapyAutophagyCancer researchOncologyBioinformaticsInternal medicinePathologyApoptosisBiology

Abstract

fetched live from OpenAlex

Rhabdomyosarcoma is a rare cancer arising in skeletal muscle that typically impacts children and young adults. It is a worldwide challenge in child health as treatment outcomes for metastatic and recurrent disease still pose a major concern for both basic and clinical scientists. The treatment strategies for rhabdomyosarcoma include multi-agent chemotherapies after surgical resection with or without ionization radiotherapy. In this comprehensive review, we first provide a detailed clinical understanding of rhabdomyosarcoma including its classification and subtypes, diagnosis, and treatment strategies. Later, we focus on chemotherapy strategies for this childhood sarcoma and discuss the impact of three mechanisms that are involved in the chemotherapy response including apoptosis, macro-autophagy, and the unfolded protein response. Finally, we discuss in vivo mouse and zebrafish models and in vitro three-dimensional bioengineering models of rhabdomyosarcoma to screen future therapeutic approaches and promote muscle regeneration.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.399
GPT teacher head0.390
Teacher spread0.009 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations75
Published2023
Admission routes2
Has abstractyes

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