Therapeutic Potential of Annexins in Sepsis and COVID-19
Bibliographic record
Abstract
Sepsis is a continuing problem in modern healthcare, with a relatively high prevalence, and a significant mortality rate worldwide. Currently, no specific anti-sepsis treatment exists despite decades of research on developing potential therapies. Annexins are molecules that show efficacy in preclinical models of sepsis but have not been investigated as a potential therapy in patients with sepsis. Human annexins play important roles in cell membrane dynamics, as well as mediation of systemic effects. Most notably, annexins are highly involved in anti-inflammatory processes, adaptive immunity, modulation of coagulation and fibrinolysis, as well as protective shielding of cells from phagocytosis. These discoveries led to the development of analogous peptides which mimic their physiological function, and investigation into the potential of using the annexins and their analogous peptides as therapeutic agents in conditions where inflammation and coagulation play a large role in the pathophysiology. In numerous studies, treatment with recombinant human annexins and annexin analogue peptides have consistently found positive outcomes in animal models of sepsis, myocardial infarction, and ischemia reperfusion injury. Annexins A1 and A5 improve organ function and reduce mortality in animal sepsis models, inhibit inflammatory processes, reduce inflammatory mediator release, and protect against ischemic injury. The mechanisms of action and demonstrated efficacy of annexins in animal models support development of annexins and their analogues for the treatment of sepsis. The effects of annexin A5 on inflammation and platelet activation may be particularly beneficial in disease caused by SARS-CoV-2 infection. Safety and efficacy of recombinant human annexin A5 are currently being studied in clinical trials in sepsis and severe COVID-19 patients.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".