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Record W3043043206 · doi:10.1113/ep088605

Combination of quercetin and exercise training attenuates depression in rats with 1,2‐dimethylhydrazine‐induced colorectal cancer: Possible involvement of inflammation and BDNF signalling

2020· article· en· W3043043206 on OpenAlexaff
Shirin Sadighparvar, Saber Ghazizadeh Darband, Bahman Yousefi, Mojtaba Kaviani, Firouz Ghaderi Pakdel, Ainaz Mihanfar, Ghader Babaei, Kazhal Mobaraki, Maryam Majidinia

Bibliographic record

VenueExperimental Physiology · 2020
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsAcadia University
Fundersnot available
Keywords1,2-DimethylhydrazineColorectal cancerMedicineDimethylhydrazineInternal medicineDepression (economics)QuercetinCancerMindfulnessInflammationEndocrinologyOncologyClinical psychologyChemistryAntioxidantBiochemistry

Abstract

fetched live from OpenAlex

New Findings What is the central question of this study? What are the alleviative effects of the combination of exercise training and quercetin supplementation on colorectal cancer‐related depression in rats with 1,2‐dimethylhydrazine‐induced colorectal cancer and what is the corresponding signalling pathway? What is the main finding and its importance? We showed that the combination of exercise training and quercetin supplementation resulted in a significant decrease in tumour incidence and improvement in depressive‐like behaviours through modulation of the BDNF/TrKβ/β‐catenin axis in the prefrontal cortex. Abstract In addition to physical problems, depression is considered to be one of the most important challenges for patients with various types of cancers, particularly colorectal cancer. Inflammation and upregulation of brain neurotrophic factors are two major links between cancer and depression. In this study, we aimed to evaluate the alleviative effects of quercetin and exercise training on depressive‐like behaviours in rats with 1,2‐dimethylhydrazine (DMH)‐induced colorectal cancer and to investigate the underlying mechanisms. Animals were assigned into the following five groups: (i) control group; (ii) DMH (20 mg kg−1s.c., once a week for 10 weeks); (iii) DMH for 10 weeks, followed by quercetin (50 mg kg−1p.o., once per week) for 12 weeks; (iv) DMH for 10 weeks, followed by exercise training for 12 weeks; and (v) DMH for 10 weeks, followed by quercetin and exercise training for 12 weeks. The DMH‐treated rats showed an increase in depressive‐like behaviours in both open field and forced swimming tests. Histopathological examination revealed neural damage and reduced Nissl bodies in the prefrontal cortex. In addition, administration of DMH increased inflammatory cytokines in the serum, prefrontal cortex and tumour tissues and decreased the expression levels of brain‐derived neurotrophic factor (BDNF), tyrosine kinase β receptor (TrKβ) and β‐catenin in the cortex. In contrast, treatment with quercetin and exercise training effectively alleviated all the above‐mentioned DMH‐associated behavioural, biochemical and histopathological alterations without changing its anti‐tumour activity. Taken together, our results show that the combination of quercetin and exercise training exerts potent anti‐tumour and anti‐depressive effects through suppression of inflammation and upregulation of the BDNF/TrKβ/β‐catenin axis in the prefrontal cortex.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.278
Teacher spread0.243 · 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

Citations38
Published2020
Admission routes1
Has abstractyes

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