Pancreatic involvement in murine antibody‐mediated transfusion‐related acute lung injury?
Bibliographic record
Abstract
Transfusion-related acute lung injury (TRALI) is a syndrome of acute respiratory distress caused by blood transfusions.1, 2 Currently, TRALI is a leading cause of blood transfusion–associated fatalities, and therapeutic strategies are not available.1, 3 The pathogenesis is multifactorial and complex, with involvement of both donor and recipient factors.1 Clinical recipient factors (the so-called first hit) are often characterized by a state of inflammation, and when combined with factors in the transfusion product (the so-called second hit) such as anti-WBC antibodies, the onset of TRALI is triggered. To obtain insights into the TRALI pathophysiology, a commonly used mouse model of antibody-mediated TRALI has been used where a potent TRALI-inducing anti–major histocompatibility complex (MHC) class I antibody called 34-1-2S is administered to a primed recipient.1 With great interest, we read the article by Tariket and colleagues4 where the authors use the 34-1-2S mouse model of TRALI based on priming with lipopolysaccharide (LPS; first hit) followed by infusion of 34-1-2S (second hit). With this model, the authors observed significant damage to the pancreas, which they described as acute lung injury–induced pancreatic degradation. Pancreatic damage was assessed via damage scores based on pancreatic tissue histology analysis (hematoxylin and eosin staining). In addition, immunoassays were conducted to measure plasma levels of the pancreatic enzymes amylase and lipase, which, when elevated, are generally considered to be reliable indicators of pancreatic injury. In our opinion, to firmly conclude that TRALI responses induce pancreas degradation, controls such as mice primed with only with LPS and mice infused with 34-1-2S alone (thus without a first hit) should be analyzed, as the antibody itself may directly target and damage the pancreas without inducing TRALI. To obtain further insights into the involvement of the pancreas in TRALI, we also performed experiments using our previously established antibody-mediated TRALI model in C57BL/6 mice.5, 6 As a first hit, we depleted CD4+T cells and primed the mice with a low dose of LPS. As a second hit, we infused 34-1-2S together with another anti–MHC class I, AF6-88.5.5.3. We observed that the lung wet-to-dry weight ratio (an established read-out for the degree of pulmonary edema) was increased when both hits were combined; however, untreated mice, mice subjected to only the first hit or mice subjected to only the second hit (Figure 1A) did not undergo TRALI-induced lung damage. Strikingly, however, we did not observe any increase in plasma amylase and lipase levels in these TRALI mice compared with untreated mice (Figure 1B, C, respectively). We also did not observe any elevation in plasma amylase and lipase levels in the mice subjected to the first or second hit only. In contrast to Tariket et al,4 based on the normal levels of plasma amylase and lipase, our findings do not support the occurrence of TRALI-induced pancreatic degradation. A possible reason for the different outcomes of both studies may perhaps be related to differences in the composition of the gut microbiota.5 As we performed CD4+ T-cell depletion before TRALI induction, in contrast to Tariket et al, it is possible that this may also have contributed to the discrepancy in the two studies. Due to the delicate nature of the pancreatic tissue, we did not consider pancreatic tissue histology analysis to be reliable in our hands. Further research is required to clarify the potential involvement of the pancreas and other organs besides the lungs in TRALI. The authors declare no conflicts of interest.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".