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Breakdown of the Apical Spectrin Network Leads to Endothelial Dysfunction and Vascular Stiffening in Pulmonary Arterial Hypertension

2023· article· en· W4367599935 on OpenAlexaff
S. Sulstarova, Anna Foley, Sivakami Mylvaganam, Benjamin E. Steinberg, Spencer A. Freeman, Neil M. Goldenberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsStiffeningCardiologySpectrinMedicineEndothelial dysfunctionInternal medicinePulmonary hypertensionMaterials scienceBiologyComposite materialGeneticsCell

Abstract

fetched live from OpenAlex

Recently, a critical role for a spectrin cytoskeletal network in sensing of shear and regulation of vascular stiffness has been described in systemic endothelial cells. The loss of spectrin resulted in the disappearance of apical caveolae, dysregulated eNOS activation, and a lack of calcium response to shear. Since spectrin can be broken down in vivo by calpain, which is activated in pulmonary arterial hypertension, I hypothesized that spectrin breakdown may represent a key mechanism underlying endothelial dysfunction.I report an increased prevalence of spectrin digesting proteases and spectrin breakdown products in the lungs of monocrotaline treated rats, along with decreased levels of caveolin-1 and a reduction in apical caveolae, a trend that was reversed by administration of calpeptin, a calpain inhibitor. Calpeptin was also shown to be protective against severe pulmonary hypertension in MCT rats. Furthermore, I determined that the knockdown of spectrin in endothelial cells results in internalization of caveolin-1 and impaired endothelial responses to shear. My studies detail a critical role for the spectrin cytoskeleton in the development of endothelial dysfunction and highlight the therapeutic potential of preventing its breakdown via calpain inhibition.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.256
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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