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Record W2758077547 · doi:10.5430/jst.v8n1p1

Relationship between salivary adiponectin, IGF-1, obesity and breast cancer

2017· article· en· W2758077547 on OpenAlexvenueno aff
Charles F. Streckfus

Bibliographic record

VenueJournal of Solid Tumors · 2017
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAdiponectinBreast cancerMedicineInternal medicineObesityCancerEndocrinologyBody mass indexInsulin resistance

Abstract

fetched live from OpenAlex

Objective: The objective of this study was to determine if adiponectin and IGF-1 salivary concentrations are altered in combination with the presence of obesity and breast cancer. The null hypothesis is that there are no significant adiponectin and IGF-1 concentration alterations secondary to the presence of obesity and/or carcinoma of the breast.Methods: There were two groups of test subjects: healthy controls (n = 20) and individuals diagnosed with breast cancer (n = 20). The two cohorts were further stratified into four groups. These included subjects who are healthy and of normal BMI (n = 10); are healthy but have an elevated BMI (n = 10); have breast cancer and a normal BMI (n = 10); and have cancer and an elevated BMI (n = 10). The presence and concentration of adiponectin and IGF-1 was determined using the ELISA methodology.Results: The investigation revealed a significant increase in mean adiponectin levels in subjects with cancer compared to the controls (t = -2.57; p < .01). Individuals that were diagnosed with breast cancer and were obese exhibited the highest concentrations (F = 5.13; p < .005) of adiponectin. Adiponectin concentrations were also found to be correlated to IGF-I levels (r = 0.05; p < .001).Conclusion: Salivary adiponectin levels were significantly higher among cancer group. There were no significant differences between the cancer and control groups for IGF-I levels.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.033
GPT teacher head0.323
Teacher spread0.290 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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