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Record W2792137812 · doi:10.1155/2018/2426712

Targeting Mitochondrial Bioenergetics as a Therapeutic Strategy for Chronic Lymphocytic Leukemia

2018· review· en· W2792137812 on OpenAlexafffund
Subir Roy Chowdhury, Versha Banerji

Bibliographic record

VenueOxidative Medicine and Cellular Longevity · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
FundersCancerCare Manitoba FoundationResearch Manitoba
KeywordsBioenergeticsMitochondrionMitochondrial biogenesisReactive oxygen speciesCancer cellBiologyOxidative stressChronic lymphocytic leukemiaCell biologyMitochondrial ROSOxidative phosphorylationBiogenesisCancer researchCancerLeukemiaBiochemistryImmunologyGeneticsGene

Abstract

fetched live from OpenAlex

Altered cellular metabolism is considered a hallmark of cancer and is fast becoming an avenue for therapeutic intervention. Mitochondria have recently been viewed as an important cellular compartment that fuels the metabolic demands of cancer cells. Mitochondria are the major source of ATP and metabolites necessary to fulfill the bioenergetics and biosynthetic demands of cancer cells. Furthermore, mitochondria are central to cell death and the main source for generation of reactive oxygen species (ROS). Overall, the growing evidence now suggests that mitochondrial bioenergetics, biogenesis, ROS production, and adaptation to intrinsic oxidative stress are elevated in chronic lymphocytic leukemia (CLL). Hence, recent studies have shown that mitochondrial metabolism could be targeted for cancer therapy. This review focuses the recent advancements in targeting mitochondrial metabolism for the treatment of CLL.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.335
Teacher spread0.285 · 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

Citations39
Published2018
Admission routes2
Has abstractyes

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