Functional linkage as a direction for studies in oxidative stress: α-adrenergic receptorsThis review is one of a selection of papers published in a Special Issue on Oxidative Stress in Health and Disease.
Bibliographic record
Abstract
The alpha-adrenergic receptors (adrenoceptors) are activated by the endogenous agonists epinephrine and norepinephrine. They are G protein-coupled receptors that may be broadly classified into alpha1 (subclasses alpha1A, alpha1B, alpha1D) and alpha2 (subclasses alpha2A, alpha2B, alpha2C). The alpha1-adrenoceptors act by binding to G(alpha)q subunits of the G proteins, causing activation of phospholipase C (PLC). PLC converts phosphatidylinositol 4,5-bisphosphate into inositol trisphosphate (IP3) and diacylglycerol (DAG), which have downstream effects on cytosolic Ca2+ concentration. The alpha2-adrenoceptors bind to G(alpha)i thus inhibiting adenylyl cyclase and decreasing cAMP levels. DAG alters protein kinase C activity and cAMP activates protein kinase A. The downstream pathways of the two receptors may also interact. Activation of alpha1- and alpha2-adrenoceptors in vascular smooth muscle results in vasoconstriction. However, the densities of individual receptor subclasses vary between vessel beds or between vessels of various sizes within the same bed. In vasculature, the densities of adrenoceptor subclasses differ between conduit arteries and arterioles. These differences, along with differences in coupling mechanisms, allow for fine regulation of arterial blood flow. This diversity is enhanced by interactions resulting from homo- and heterodimer formation of the receptors, metabolic pathways, and kinases. Reactive oxygen species generated in pathologies may alter alpha1- and alpha2-adrenoceptor cascades, change vascular contractility, or cause remodeling of blood vessels. This review emphasizes the need for understanding the functional linkage between alpha-adrenoceptor subtypes, coupling, cross talk, and oxidative stress in cardiovascular pathologies.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".