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Record W2742308454 · doi:10.3390/nu9080859

Bridging the Gap between Gut Microbial Dysbiosis and Cardiovascular Diseases

2017· review· en· W2742308454 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNutrients · 2017
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsHamilton Health SciencesMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsDysbiosisBridging (networking)Gut floraBiologyMedicineMicrobiologyImmunologyComputer science

Abstract

fetched live from OpenAlex

The human gut is heavily colonized by a community of microbiota, primarily bacteria, that exists in a symbiotic relationship with the host and plays a critical role in maintaining host homeostasis. The consumption of a high-fat (HF) diet has been shown to induce gut dysbiosis and reduce intestinal integrity. Recent studies have revealed that dysbiosis contributes to the progression of cardiovascular diseases (CVDs) by promoting two major CVD risk factors-atherosclerosis and hypertension. Imbalances in host-microbial interaction impair homeostatic mechanisms that regulate health and can activate multiple pathways leading to CVD risk factor progression. Dysbiosis has been implicated in the development of atherosclerosis through metabolism-independent and metabolite-dependent pathways. This review will illustrate how these pathways contribute to the various stages of atherosclerotic plaque progression. In addition, dysbiosis can promote hypertension through vascular fibrosis and an alteration of vascular tone. As CVD is the number one cause of death globally, investigating the gut microbiota as a locus of intervention presents a novel and clinically relevant avenue for future research, with vast therapeutic potential.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
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 head0.107
GPT teacher head0.356
Teacher spread0.249 · 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