Anti-Human Tissue Factor Antibody Ameliorated Intestinal Ischemia Reperfusion-Induced Acute Lung Injury in Human Tissue Factor Knock-In Mice
Bibliographic record
Abstract
BACKGROUND: Interaction between the coagulation and inflammation systems plays an important role in the development of acute respiratory distress syndrome (ARDS). Anti-coagulation is an attractive option for ARDS treatment, and this has promoted development of new antibodies. However, preclinical trials for these antibodies are often limited by the high cost and availability of non-human primates. In the present study, we developed a novel alternative method to test the role of a humanized anti-tissue factor mAb in acute lung injury with transgenic mice. METHODOLOGY/PRINCIPAL FINDINGS: Human tissue factor knock-in (hTF-KI) transgenic mice and a novel humanized anti-human tissue factor mAb (anti-hTF mAb, CNTO859) were developed. The hTF-KI mice showed a normal and functional expression of hTF. The anti-hTF mAb specifically blocked the pro-coagulation activity of brain extracts from the hTF-KI mice and human, but not from wild type mice. An extrapulmonary ARDS model was used by intestinal ischemia-reperfusion. Significant lung tissue damage in hTF-KI mice was observed after 2 h reperfusion. Administration of CNTO859 (5 mg/kg, i.v.) attenuated the severity of lung tissue injury, decreased the total cell counts and protein concentration in bronchoalveolar lavage fluid, and reduced Evans blue leakage. In addition, the treatment significantly reduced alveolar fibrin deposition, and decreased tissue factor and plasminogen activator inhibitor-1 activity in the serum. This treatment also down-regulated cytokine expression and reduced cell death in the lung. CONCLUSIONS: This novel anti-hTF antibody showed beneficial effects on intestinal ischemia-reperfusion induced acute lung injury, which merits further investigation for clinical usage. In addition, the use of knock-in transgenic mice to test the efficacy of antibodies against human-specific proteins is a novel strategy for preclinical studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".