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Record W2790535254 · doi:10.1139/facets-2017-0058

Metallothionein expression in slow- vs. fast-twitch muscle fibers following 4 weeks of streptozotocin-induced type 1 diabetes

2018· article· en· W2790535254 on OpenAlexafffundvenue
Pamela Mondragon, Andreas Bergdahl

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsStreptozotocinSoleus muscleInternal medicineEndocrinologyMetallothioneinImmunostainingOxidative stressSkeletal musclePlantaris muscleDiabetes mellitusChemistryBlotDownregulation and upregulationType 1 diabetesBiologyImmunohistochemistryBiochemistryMedicineGene

Abstract

fetched live from OpenAlex

Type 1 diabetes (T1DM) is known to cause an increase in reactive oxygen species (ROS) and elevated intracellular glucose levels. We investigated the metallothionein I and II (MT I+II) antioxidants expression in soleus (mainly slow-twitch) and plantaris (predominantly fast-twitch) skeletal muscle using a rodent model of streptozotocin-induced diabetes. The presence of oxidative stress was confirmed by the detection of increased levels of protein carbonyl formation in the diabetic tissues. DAB (3,3′-diaminobenzidine) immunostaining and Western blotting analyses demonstrated that MT I+II expression was significantly upregulated in the diabetic soleus and plantaris muscle tissues compared with their respective controls. Moreover, no significant difference was detected between the plantaris and soleus controls or between the plantaris and soleus diabetic tissues. These findings suggest that there is an increase in MT protein expression in the soleus and plantaris muscles associated with the induction of T1DM. A better understanding of the molecular mechanisms that allow MT to prevent the oxidative stress associated with diabetes could lead to a novel therapeutic strategy for this chronic disease and its associated complications.

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 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 score0.132
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.311
Teacher spread0.287 · 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 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

Citations3
Published2018
Admission routes3
Has abstractyes

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