MétaCan
Menu
Back to cohort
Record W4405538336 · doi:10.1101/2024.12.16.24319108

Intermittent fasting alters tumor burden, autophagy, and metabolites in chronic lymphocytic leukemia

2024· preprint· en· W4405538336 on OpenAlexafffund
Eleah Stringer, Zhengxiao Wei, Samantha Punch, Nathalie Costie, Jun Han, David R. Goodlett, Farouk S. Nathoo, Julian J. Lum, Nicol Macpherson

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersCanadian Institutes of Health ResearchBC Cancer Foundation
KeywordsAutophagyChronic lymphocytic leukemiaLeukemiaCancer researchMedicineBiologyInternal medicineBiochemistryApoptosis

Abstract

fetched live from OpenAlex

ABSTRACT Emerging preclinical data suggests dietary interventions, including intermittent fasting, may play a key role in altering cancer progression. In a feasibility trial monitoring cellular, clinical, and qualitative changes, ten patients with chronic lymphocytic leukemia followed time-restricted eating for three months, and five patients for six months. Seven of fifteen participants (47%) experienced a decrease or stabilization in malignant lymphocyte counts on time-restricted eating, while malignant lymphocyte accumulation slowed in five participants (33%) or had no effect in three participants (20%). A reduction in malignant lymphocyte counts were accompanied by an unexpected decline in cellular autophagy in malignant lymphocytes. Metabolite profiling identified microbial-derived bile acid metabolites, glycoursodeoxycholic, taurochenodeoxycholic, glycolithocholic and ursodeoxycholic acid, and short-chain fatty acids, dehydrolithocholic and apocholic acid, that changed with time-restricted eating. Participant quality of life improved during time-restricted eating. These results connect time-restricted eating with shifts in autophagy, microbial-derived metabolites, tumor progression, and quality of life.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.021
GPT teacher head0.304
Teacher spread0.283 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venuemedRxivSame topicDietary Effects on HealthFrench-language works237,207