Ventilator-induced coagulopathy in experimental<i>Streptococcus pneumoniae</i>pneumonia
Bibliographic record
Abstract
Pneumonia, the main cause of acute lung injury, is characterised by a local pro-inflammatory response and coagulopathy. Mechanical ventilation (MV) is often required. However, MV can lead to additional injury: so-called ventilator-induced lung injury (VILI). Therefore, the current authors investigated the effect of VILI on alveolar fibrin turnover in Streptococcus pneumoniae pneumonia. Pneumonia was induced in rats, followed 48 h later by either lung-protective MV (lower tidal volumes (LV(T)) and positive end-expiratory pressure (PEEP)) or MV causing VILI (high tidal volumes (HV(T)) and zero end-expiratory pressure (ZEEP)) for 3 h. Nonventilated pneumonia rats and healthy rats served as controls. Thrombin-antithrombin complexes (TATc), as a measure for coagulation, and plasminogen activator activity, as a measure of fibrinolysis, were determined in bronchoalveolar lavage fluid (BALF) and serum. Pneumonia was characterised by local (BALF) activation of coagulation, resulting in elevated TATc levels and attenuation of fibrinolysis compared with healthy controls. LV(T)-PEEP did not influence alveolar coagulation or fibrinolysis. HV(T)-ZEEP did intensify the local procoagulant response: TATc levels rose significantly and levels of the main inhibitor of fibrinolysis, plasminogen activator inhibitor-1, increased significantly. HV(T)-ZEEP also resulted in systemic elevation of TATc compared with LV(T)-PEEP. Mechanical ventilation causing ventilator-induced lung injury increases pulmonary coagulopathy in an animal model of Streptococcus pneumoniae pneumonia and results in systemic coagulopathy.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".