Abstract P3001: Regulation Of Perivascular Adipose Tissue By Follistatin In Hypertension
Bibliographic record
Abstract
Follistatin lowers BP and improves resistance artery health in spontaneously hypertensive rats (SHR), a model of essential HTN. Functionally, follistatin improves smooth muscle contraction and endothelium-dependent relaxation. Structurally, increased medial collagen was inhibited. Vascular ROS were also reduced. Perivascular adipose tissue (PVAT) regulates vascular tone via release of vasoactive substances. Oxidative stress in PVAT, as in HTN, induces release of contractile agents. Transition from white to brown PVAT is associated with decreased risk of CVD. We analyze effects of follistatin on PVAT and its regulation of vessel function in SHR and normotensive Wistar Kyoto (WKY) rats. White and brown PVAT were dissected from treated rats; arteries were isolated from untreated donors. PVAT was introduced near strain-matched donor arteries and assessed for ROS levels and contractility using DHE and wire myography, respectively. In some vessels, endothelium was denuded to assess its contribution to contraction. Follistatin reduced ROS, hypercontractility, and improved relaxation in a PVAT-dependent manner. Brown PVAT induced significant anti-contractile and antioxidant effects. Elevated expression of adipose tissue browning markers and decreased adipocyte area with follistatin, support PVAT browning effects. Endothelium removal results in augmented vessel constriction, effects reduced by follistatin. Follistatin improves SHR smooth muscle tone in a PVAT-dependent manner, likely through the inhibition of vascular ROS and browning of PVAT. Future work will profile PVAT effects of follistatin through unbiased proteomic analysis. Although most vessels are surrounded by PVAT, studies of PVAT-vessel interaction in HTN are few. By characterizing PVAT dysfunction, we aim to outline specific PVAT-mediated pathways involved in lowering BP and improving vascular function and structure. Results will provide new insights into PVAT-targeted therapy for essential HTN, allowing potential translation of benefits to obesity and other diseases exhibiting PVAT dysfunction.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".