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Record W2795205220 · doi:10.1371/journal.pone.0195411

Chronic noise-exposure exacerbates insulin resistance and promotes the manifestations of the type 2 diabetes in a high-fat diet mouse model

2018· article· en· W2795205220 on OpenAlexaff
Lijie Liu, Yi Huang, Cong Fang, Hongyu Zhang, Jingyu Yang, Chuanying Xuan, Fanfan Wang, Haiying Lu, Shuangfeng Cao, Yongfang Wang, Shengwei Li, Jun Sha, Mingming Zha, Min Guo, Jian Wang

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsDalhousie University
FundersSoutheast UniversityNational Natural Science Foundation of China
KeywordsInsulin resistanceInternal medicineEndocrinologyDyslipidemiaType 2 diabetesType 2 Diabetes MellitusDiabetes mellitusMedicineLipolysisInsulinNoise (video)Adipose tissue

Abstract

fetched live from OpenAlex

Epidemiological studies have revealed that noise exposure was associated with an increased risk of type 2 diabetes mellitus (T2DM). However, the exact nature of that association remains to be elucidated. The present study is designed to examine the effects of chronic noise exposure on the development of T2DM in combination with a high-fat-diet (HFD) in mice. Here we show that chronic noise exposure at 85 dB SPL (4 h /day, below the safety limit for occupational noise exposure) exaggerated multiple metabolic abnormalities induced by HFD in C57BL/6J male mice, including worsened glucose intolerance, insulin resistance, fasting hyperglycemia and dyslipidemia. Furthermore, noise exposure exhibited a paradoxical impact on fat accumulation and circulating levels of free fatty acid, indicating a potential stimulating effect of noise on lipolysis. These results provide first in vivo supporting evidence for the causative role of noise exposure in diabetogenesis and pinpoint a noise-associated increase in blood free fatty acid levels as a possible mediator accelerating the effect of noise on the development of insulin resistance and T2DM.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.040
GPT teacher head0.293
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations29
Published2018
Admission routes1
Has abstractyes

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