Bibliographic record
Abstract
On the outer surface of a human cell there is a dense layer of complex carbohydrates called glycocalyx, also referred to as glycans or the sugar coating on the cell surface, which is composed of a complex array of oligosaccharide and polysaccharide glucose chains that are covalently bonded to proteoglycans and lipids bound to the cell membrane surface. Studies of an intact endothelial glycocalyx layer (EGL) have revealed a number of critical functions that relate the importance of this protective layer to vascular integrity and permeability. These functions include the following: stabilization and maintenance of the vascular endothelium, an active reservoir of essential plasma proteins (i.e., albumin, antithrombin, heparan sulfate, and antioxidants), a buffer zone between the blood (formed elements) and the surface of the endothelium, and a mechanotransducer to detect changes in shear stress that facilitate vascular tone. There have been numerous review articles about the structure and function of endothelial glycocalyx over the past two decades, yet there still remains a significant knowledge gap in the perfusion literature around the importance of EGL. Perioperative fluid management and gaseous microemboli can both contribute to the damage/degradation of endothelial glycocalyx. A damaged EGL can result in systemic and myocardial edema, platelet and leukocyte adhesion, fluid extravasation, and contributes to microvascular perfusion heterogeneity. Knowledge of the importance of endothelial glycocalyx will enable clinicians to have a better understanding of the impact of gaseous microbubbles, hyperoxia, and ischemic reperfusion injury during cardiac surgery. The purpose of this article is to provide an in depth review of the EGL and how this protective barrier impacts the microcirculation, fluid homeostasis, inflammation, and edema during cardiac surgery.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".