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Record W7061252260

Phthalates and non-phthalates plasticizers disrupt lipid metabolism

2021· other· en· W7061252260 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2021
Typeother
Languageen
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPhthalatePlasticizerEndocrine disruptorDiethyl phthalatePhthalic acidBisphenol AMetabolismToxicityDibutyl phthalate
DOInot available

Abstract

fetched live from OpenAlex

Plasticizers are synthetic organic chemicals that are used in different products to make them flexible, elastic, and durable. Plasticizers are not attached to the products by covalent bonds, as a result, they leach out from the products leading to environmental contamination. The most widely used plasticizer, Di-(2-ethyl hexyl) phthalate (DEHP), have been restricted from general use in the EU, Canada, and the USA due to their reported toxicity. The alternative plasticizer, Di-(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), was introduced to the European market as a safer alternative for endocrine-disrupting phthalates such as DEHP, and diisononyl phthalate (DINP). According to the current toxicological data, DINCH is neither an endocrine disruptor and nor a reproductive toxicant. Thus, DINCH was approved for use in food contact containers, and in children’s toys. The increase in global demand for alternative plasticizers led to their buildup in the environment and an increase in DINCH exposure. The lack of toxicity data and safety assessment of DINCH has raised the concern to human and animal health. Due to the similar structure of DEHP, DINP, and DINCH, we suggest that DINCH can be classified as a metabolic disruptor that alters fat metabolism and induces adipogenesis. In this study, we investigated the negatives effects of DINCH (at concentrations ranging from 0.01 to 10 μM) compared it to phthalates DINP, DEHP, Dibutyl phthalate (DBP), and Diethyl phthalate (DEP) at the early developmental stages of zebrafish. We further analyzed DINCH and DINP using the mouse preadipocyte cells 3T3L1. We found that DINCH and DEHP caused hatching delay in a dose-dependent manner. Behavioral analysis of larvae demonstrated that DEHP, DBP, DEP, and DINCH impair motor activity. The Oil Red O lipid staining showed a slight lipid accumulation in larval zebrafish at different DINP and DINCH concentrations. To further confirm the findings, qPCR was performed to analyze lipid metabolism genes. DINCH and DINP altered lipid metabolism genes including, fasn, srebp, pparg etc. The oxidative stress state imposed by DINCH exposure was shown by a slight increase in superoxide dismutase enzymatic activity and the alteration on stress-related genes. In 3T3L1 cells, 10 and 100 μM of DINCH and DINP exposure induced lipid droplets formation like that induced by 100 nM rosiglitazone. Genes associated with lipid metabolism and lipid transport were altered by DINCH and DINP. These results indicate that DINCH exposure could induce physiological and metabolic toxicity. The data presented in this thesis could provide crucial information for further toxicological assessment.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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