Using ex vivo bioengineered lungs to model pathologies and screening therapeutics: A proof‐of‐concept study
Bibliographic record
Abstract
Respiratory diseases, claim over eight million lives annually. However, the transition from preclinical to clinical phases in research studies is often hindered, partly due to inadequate representation of preclinical models in clinical trials. To address this, we conducted a proof-of-concept study using an ex vivo model to identify lung pathologies and to screen therapeutics in a humanized rodent model. We extracted and decellularized mouse heart-lung tissues using a detergent-based technique. The lungs were then seeded and cultured with human cell lines (BEAS-2B, A549, and Calu3) for 6-10 days, representing healthy lungs, cancerous states, and congenital pathologies, respectively. By manipulating cultural conditions and leveraging the unique characteristics of the cell lines, we successfully modeled various pathologies, including advanced-stage solid tumors and the primary phase of SARS-CoV-2 infection. Validation was conducted through histology, immunofluorescence staining, and pathology analysis. Additionally, our study involved pathological screening of the efficacy and impact of key anti-neoplastic therapeutics (Cisplatin and Wogonin) in cancer models. The results highlight the versatility and strength of the ex vivo model in representing crucial lung pathologies and screening therapeutics during the preclinical phase. This approach holds promise for bridging the gap between preclinical and clinical research, aiding in the development of effective treatments for respiratory diseases, including lung cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".