Measurement of Phospholipase D Activation in Vascular Smooth Muscle Cells
Bibliographic record
Abstract
Phospholipase D (PLD), which hydrolyzes phospholipids (primarily phos-phatidylcholine) to generate phosphatidic acid, is an essential component in cellular signal transduction (1,2). Phosphatidic acid and its dephosphorylated product 1,2 diacylglycerol, are important intracellular second messengers that play critical roles in various cell types including vascular smooth muscle cells (3,4). The human PLD gene 1 has been cloned and expressed. The expressed mammalian PLD has a molecular weight of approx 120 and has both catalytic and transphosphatidylation activities with phosphatidyl cho-line as substrate (5). PLD activation can be initiated by various agonists that fall into two main categories: (1) agents that signal through tyrosine kinase-dependent pathways, e.g., growth factors, and (2) agents that signal through seven transmembrane receptors, acting through trimeric membrane G proteins, e.g., angiotensin II and endothelin-1 (6-9). The molecular mechanisms by which Ang II receptors couple to PLD have recently been identified. The Gβγ subunits as well as their associated Gα(12) subunits, mediate Ang II-induced PLD activation via Src-dependent mechanisms in vascular smooth muscle cells. Small molecular-weight G protein RhoA is also involved in these signaling cascades (10).
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".