Sensing Dying Cells in Health and Disease
Bibliographic record
Abstract
Kidney injury molecule-1 (KIM-1), also known as T-cell Ig and mucin domain-1 (TIM-1), is a widely recognized biomarker for AKI, but its biological function is less appreciated. KIM-1/TIM-1 belongs to the T-cell Ig and mucin domain family of conserved transmembrane proteins, which bear the characteristic six-cysteine Ig-like variable domain. The latter enables binding of KIM-1/TIM-1 to its natural ligand, phosphatidylserine, expressed on the surface of apoptotic cells and necrotic cells. KIM-1/TIM-1 is expressed in a variety of tissues and plays fundamental roles in regulating sterile inflammation and adaptive immune responses. In the kidney, KIM-1 is upregulated on injured renal proximal tubule cells, which transforms them into phagocytes for clearance of dying cells and helps to dampen sterile inflammation. TIM-1, expressed in T cells, B cells, and natural killer T cells, is essential for cell activation and immune regulatory functions in the host. Functional polymorphisms in the gene for KIM-1/TIM-1, HAVCR1 , have been associated with susceptibility to immunoinflammatory conditions and hepatitis A virus-induced liver failure, which is thought to be due to a differential ability of KIM-1/TIM-1 variants to bind phosphatidylserine. This review will summarize the role of KIM-1/TIM-1 in health and disease and its potential clinical applications as a biomarker and therapeutic target in humans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".