MétaCan
Menu
Back to cohort
Record W2467293278

Dietary Protective Effects Against Hepatocellular Carcinoma Development in Mdr2-/- Knockout Mice.

2017· article· en· W2467293278 on OpenAlexaff
Maria Pia Gentileschi, Claudia Lattanzio, Francesco Menicagli, Bruno Vincenzi, Giovanni Cigliana, Alfonso Baldi, Giovanni Blandino, Paola Muti, Maurizio Fanciulli, Enrico P. Spugnini

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCalorieHepatocellular carcinomaMedicineInternal medicineCalorie restrictionKnockout mouseGastroenterologyPhysiologyEndocrinologyReceptor
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: The Mdr2(-/-) mouse develops early chronic cholestatic hepatitis and hepatocellularcarcinoma (HCC) when adult. We tested the effects of a restricted-calorie diet on HCC development in Mdr2(-/-) mice. MATERIALS AND METHODS: Mdr2(-/-) mice (n=40, divided into two groups of 20 mice each) were randomized to receive ad libitum diet or restricted-calorie diet. Two mice from each group were sacrificed at 3 and 6 months, and liver tissue samples were removed for analysis. The remaining mice were fed their respective diets until the age of 30 months, at which time they were euthanized and livers were collected for analysis. RESULTS: The restricted-calorie diet had partial chemopreventive effect on the development of HCC in Mdr2(-/-) mice. Moreover, mice with ad libitum diet had a median survival of 361 days, while the restricted-calorie group had a median survival of 500 days (p=0.0001). CONCLUSION: A restricted diet might reduce the chance of developing HCC in patients at risk and could increase the protective action of anti-inflammatory agents.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.236
Teacher spread0.203 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicLiver physiology and pathologyFrench-language works237,207