The calcium-sensitive receptor in the pathogenesis of sepsis
Bibliographic record
Abstract
Sepsis is an organ dysfunction caused by the body's dysfunctional response to infection, which is one of the most important causes of death in critically ill patients. It is characterized by high morbidity, high mortality, and high treatment costs. Currently, the treatment of sepsis relies mainly on supportive therapy, and there is a lack of targeted intervention ways. Studying the pathogenesis of sepsis and exploring new therapeutic targets are of great theoretical and clinical importance. Calcium-sensitive receptor (CaSR) is a cell membrane receptor belonging to the family of G protein-coupled receptors and is widely distributed in various tissues and organs. Research indicates that CaSR plays a role in mitigating sepsis-induced organ dysfunction, exhibiting tissue-specific protective effects in certain tissues while inducing inflammatory responses in others. Elucidating these dual effects and the underlying signaling pathways could facilitate the development of targeted therapies for sepsis-related organ damage. This review summarizes recent literature and evidence on CaSR signaling in sepsis-induced organ dysfunction. In addition, we provide an up-to-date schematic of the most important and likely molecular signaling pathways associated with CaSR in sepsis.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".