Smart imaging of acute lung injury: exploration of myeloperoxidase activity using in vivo endoscopic confocal fluorescence microscopy
Bibliographic record
Abstract
The pathophysiology of acute lung injury (ALI) is well characterized, but its real-time assessment at bedside remains a challenge. When patients do not improve after 1 wk despite supportive therapies, physicians have to consider open lung biopsy (OLB) to identify the process(es) at play. Sustained inflammation and inadequate repair are often observed in this context. OLB is neither easy to perform in a critical setting nor exempt from complications. Herein, we explore intravital endoscopic confocal fluorescence microscopy (ECFM) of the lung in vivo combined with the use of fluorescent smart probe(s) activated by myeloperoxidase (MPO). MPO is a granular enzyme expressed by polymorphonuclear neutrophils (PMNs) and alveolar macrophages (AMs), catalyzing the synthesis of hypoclorous acid, a by-product of hydrogen peroxide. Activation of these probes was first validated in vitro in relevant cells (i.e., AMs and PMNs) and on MPO-non-expressing cells (as negative controls) and then tested in vivo using three rat models of ALI and real-time intravital imaging with ECFM. Semiquantitative image analyses revealed that in vivo probe-related cellular/background fluorescence was associated with corresponding enhanced lung enzymatic activity and was partly prevented by specific MPO inhibition. Additional ex vivo phenotyping was performed, confirming that fluorescent cells were neutrophil elastase(+) (PMNs) or CD68(+) (AMs). This work is a first step toward "virtual biopsy" of ALI without OLB.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".