Lipid microdomains in model and biological membranes: how strong are the connections?
Bibliographic record
Abstract
1. Introduction 373 2. Are rafts probable? 374 3. Micro-, nano- or ephemeral domains? 375 4. How can we reliably assess ‘raft’ composition? 376 5. Are rafts plausible? 379 6. What more can model systems contribute to ‘raft’ studies? 381 7. References 382 The concept of ‘lipid rafts’ and related liquid-ordered membrane microdomains has attracted great attention in the field of membrane biology, both as a novel paradigm in models of membrane organization and for the potential importance of such domains in phenomena such as membrane signaling and the differential trafficking of various membrane components. Studies of biological and of model membranes have gone hand in hand in shaping our current picture of the possible organization and functions of liquid-ordered lipid microdomains in membranes. This essay discusses some important current questions concerning the existence and functional importance of lipid microdomains in mammalian cell membranes, and the potential as well as the limitations of using model systems to help to address such questions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".