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Record W4312209891 · doi:10.3390/metabo12121284

Cancer Cachexia and Dysregulated Phosphate Metabolism: Insights from Mutant p53 and Mutant Klotho Mouse Models

2022· article· en· W4312209891 on OpenAlexaff
Ronald B. Brown

Bibliographic record

VenueMetabolites · 2022
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCachexiaKlothoCarcinogenesisBiologyCancerCancer researchWastingAnabolismEndocrinologyInternal medicineMedicineKidneyGenetics

Abstract

fetched live from OpenAlex

The present perspective article proposes that cachexia, muscle wasting in cancer, is mediated by dysregulated phosphate metabolism and phosphate toxicity that can damage tissues in most major organ systems. A diet high in phosphorus fed to mice deficient in klotho, a cofactor that regulates phosphate metabolism, accelerates aging, sarcopenia, general organ atrophy, kyphosis, and osteoporosis. Similar effects are seen in phenotypes of mutant p53 mice that overexpress the p53 tumor suppressor gene. Although mutant p53 mice do not develop tumors compared to wild-type mice, mutant p53 mice have shorter mean lifespans. Furthermore, tumorigenesis is associated with the sequestration of excessive inorganic phosphate, and dangerous levels of phosphate are released into circulation during tumor lysis syndrome. In total, this evidence implies that tumorigenesis may be a compensatory mechanism that provides protective effects against systemic exposure to dysregulated phosphate metabolism and phosphate toxicity related to cachexia in cancer. Moreover, the hypothetical protection against phosphate toxicity afforded by tumorigenesis also provides an alternate explanation for putative tumor evasion of the immune system. Insights proposed in this perspective paper provide new directions for further research, with potential to develop novel interventions and clinical applications that modify dietary phosphate intake to reduce cachexia in cancer patients.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.252
Teacher spread0.236 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2022
Admission routes1
Has abstractyes

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