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The Role of Genetics and Synergistic Effect of Targeting Common GeneticMutations in Acute Lymphoblastic Leukemia (ALL)

2022· review· en· W4311200937 on OpenAlexaff
Niloofar Pilehvari, Maryam Katoueezadeh, Gholamhossein Hassanshahi, Seyedeh Atekeh Torabizadeh

Bibliographic record

VenueMini-Reviews in Medicinal Chemistry · 2022
Typereview
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsBrock University
Fundersnot available
KeywordsLymphoblastic LeukemiaGeneticsMutationBiologyCancer researchMedicineLeukemiaGene

Abstract

fetched live from OpenAlex

Increasing concern regarding non-treatment and relapse in Acute Lymphoblastic Leukemia (ALL) among children and adults has attracted the attention of researchers to investigate the genetic factors of ALL and discover new treatments with a better prognosis. Nevertheless, the survival rate in children is more than in adults; therefore, it is necessary to find new potential molecular targets with better therapeutic results. Genomic analysis has enabled the detection of different genetic defects that are serious for driving leukemogenesis. The study of genetic translocation provides a better understanding of the function of genes involved in disease progression. This paper presents an overview of the main genetic translocations and dysregulations in the signaling pathways of ALL. We also report the inhibitors of these main translocations and evaluate the synergistic effect of chemical inhibitors and gamma-ray irradiation on ALL.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.343
Teacher spread0.321 · 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 teacher head, not a consensus.

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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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