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Record W3167474436 · doi:10.1093/ajh/hpab094

Inflammation: A Mediator Between Hypertension and Neurodegenerative Diseases

2021· article· en· W3167474436 on OpenAlexafffund
Jessica Youwakim, Hélène Girouard

Bibliographic record

VenueAmerican Journal of Hypertension · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsMedicineNeuroinflammationInflammationStroke (engine)DiseasePathophysiologyNeuroscienceBlood–brain barrierSystemic inflammationPathophysiology of hypertensionMediatorBioinformaticsCentral nervous systemImmunologyBlood pressurePathologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

Hypertension is the most prevalent and modifiable risk factor for stroke, vascular cognitive impairment, and Alzheimer's disease. However, the mechanistic link between hypertension and neurodegenerative diseases remains to be understood. Recent evidence indicates that inflammation is a common pathophysiological trait for both hypertension and neurodegenerative diseases. Low-grade chronic inflammation at the systemic and central nervous system levels is now recognized to contribute to the physiopathology of hypertension. This review speculates that inflammation represents a mediator between hypertension and neurodegenerative diseases, either by a decrease in cerebral blood flow or a disruption of the blood-brain barrier which will, in turn, let inflammatory cells and neurotoxic molecules enter the brain parenchyma. This may impact brain functions including cognition and contribute to neurodegenerative diseases. This review will thus discuss the relationship between hypertension, systemic inflammation, cerebrovascular functions, neuroinflammation, and brain dysfunctions. The potential clinical future of immunotherapies against hypertension and associated cerebrovascular risks will also be presented.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.641

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 communication0.0000.001
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 head0.244
Teacher spread0.204 · 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

Citations42
Published2021
Admission routes2
Has abstractyes

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Same venueAmerican Journal of HypertensionSame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207