Identification of circulating T‐cell immunoglobulin and mucin domain 4 as a potential biomarker for coronary heart disease
Bibliographic record
Abstract
Efferocytosis, the process of engulfing and removing apoptotic cells, is attenuated in vulnerable plaques of advanced atherosclerosis. T-cell immunoglobulin and mucin domain 4 (TIMD4) is a recognition receptor protein for efferocytosis that has been implicated in atherosclerosis mouse models. However, the role of serum-soluble TIMD4 (sTIMD4) in coronary heart disease (CHD) remains unknown. In this study, we analyzed serum samples collected from two groups: Group 1 (36 healthy controls and 70 CHD patients) and Group 2 (44 chronic coronary syndrome [CCS]) and 81 acute coronary syndrome [ACS] patients). We found that sTIMD4 levels in patients with CHD were significantly higher than those in healthy controls and were also higher in ACS than in CCS patients. The area under the receiver operating characteristic curve was 0.787. Furthermore, our in vitro results showed that low-density lipoprotein/lipopolysaccharide activated p38 mitogen-activated protein kinase, which in turn enhanced a disintegrin and metalloproteinase 17, resulting in increased secretion of sTIMD4. This impairment of macrophage efferocytosis promoted inflammation. Thus, this study is not only the first identification of a potential novel biomarker of CHD, sTIMD4, but also demonstrated its pathogenesis mechanism, providing a new direction for the diagnosis and treatment of CHD.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".